Who This Template Is For
This template is for product teams building internal search experiences where findability directly affects user productivity and satisfaction. It works best for e-commerce catalogs, knowledge bases, marketplace listings, SaaS platforms with large content libraries, and enterprise tools with complex data sets.
If users rely on site navigation instead of search, or if search abandonment rates are high, the problem is usually the search results experience rather than the search algorithm.
What a Good Search Results Wireframe Must Solve
Search results pages sit between user intent and content fulfillment. The wireframe must answer:
- How quickly can a user assess whether the top results match their intent?
- What filtering and refinement tools help users narrow results without starting over?
- How does the page communicate when results are poor, empty, or ambiguous?
- What happens when results span multiple content types with different display needs?
Core Blocks to Include
1. Search Input with Suggestions
A prominent search bar with autocomplete suggestions that appear after two to three characters. Include recent searches, popular queries, and category suggestions. On mobile, the search bar should expand to full-width when focused.
2. Results Count and Sort Controls
Display total results count immediately after search submission. Provide sort options: relevance, date, popularity, and category. Show the active sort clearly to help users understand result ordering.
3. Filter Panel
Design a sidebar filter panel on desktop and a collapsible filter sheet on mobile. Include category filters, date range, content type, and any domain-specific attributes. Show active filter count and provide a clear-all option.
4. Result Cards
Each result card should display: title with keyword highlighting, excerpt with match context, content type indicator, date, and relevant metadata. The card format should be dense enough for quick scanning but not so compact that intent assessment is impossible.
5. Pagination or Infinite Scroll Controls
Choose between pagination and infinite scroll based on use case. Pagination works better for research tasks where users need to return to a specific position. Infinite scroll works better for browsing tasks. Always show position context: "Showing 1-20 of 342 results."
6. No Results and Low Results States
Design explicit wireframes for: zero results with spelling suggestions and alternative queries, low results with broader category recommendations, and error states when the search service is unavailable.
Build Process You Can Run This Week
Step 1: Catalog searchable content types
List every content type the search should index: articles, products, users, categories, help docs, and any domain-specific entities. Note which types require different card layouts.
Step 2: Define result card variants
Design card layouts for each content type. Products need images and prices. Articles need excerpts and dates. Users need avatars and roles. Create a flexible card system that accommodates these differences.
Step 3: Map filter relevance
Determine which filters apply to which content types. Category filters for products. Date filters for articles. Role filters for users. Display only relevant filters based on active content type or search context.
Step 4: Design the suggestion experience
Plan what appears before results load: popular searches, recent searches, trending content, and category shortcuts. This pre-results experience reduces failed searches by guiding users toward valid queries.
Step 5: Wireframe edge states
Create explicit designs for: zero results with corrective guidance, partial results with extension suggestions, slow loading with progressive result display, and search service unavailable with fallback options.
Step 6: Define tracking and iteration hooks
Annotate search analytics events: query submitted, suggestion clicked, filter applied, result clicked position, search abandoned, and zero-result queries. These metrics drive iterative search quality improvement.
Practical QA Checklist
- Can a user assess result relevance within three seconds per result card?
- Are keyword matching highlights visible in result excerpts?
- Do filters reduce results instantly without full page reload?
- Is the zero-results experience helpful rather than a dead end?
- Does the mobile filter experience work with one-handed interaction?
- Are sort controls visible and their effect on results clear?
- Can users return to their search position after viewing a result?
Common Mistakes and Fixes
Mistake: Results without context excerpts
Fix: display excerpt text with keyword highlighting so users can assess relevance without clicking through every result.
Mistake: Hiding filter counts
Fix: show the number of results each filter value would produce. This helps users choose useful filters and avoid zero-result dead ends.
Mistake: No guidance for empty results
Fix: offer spelling corrections, alternative queries, and category browsing when search returns zero results. Never show a blank page.
Mistake: Inconsistent card layouts across content types
Fix: use a flexible card system with standardized dimensions. Accommodate content type differences through modular card sections, not fundamentally different layouts.
Mistake: Search suggestions that do not update
Fix: refresh suggestions based on partial query input. Static suggestions become irrelevant after two to three characters.
Example: E-commerce Product Search
For an e-commerce platform search, this template can organize:
- search bar with product name and category suggestions,
- results count with sort by relevance, price, and popularity,
- sidebar filters for category, price range, brand, rating, and availability,
- product cards with image, title, price, rating, and availability indicator,
- paginated results with twenty items per page and visible page position,
- zero results state suggesting category browsing and related products.
FAQ
Should we use pagination or infinite scroll?
Pagination for task-oriented search where users need to revisit specific result positions. Infinite scroll for discovery and browsing tasks where position tracking is less important.
How many filters should we show?
Show three to five primary filters visible by default. Group additional filters under an "More filters" expansion. Too many visible filters overwhelm users and reduce usage.
How do we handle multi-word queries?
Display results matching all query terms ranked by relevance. Show "Did you mean" suggestions for potential misspellings. Support phrase search with quotation marks for exact matching.
What search analytics should we track?
Track: queries per session, zero-result rate, suggestion acceptance rate, click position in results, filter usage frequency, and search-to-conversion rate.
Related Reading
- User Flow Mapping
- Responsive Preview
- Marketplace Listing Template
- Edge State Planning Guide
- Mobile-First Planning Guide
- Wireframe Best Practices
- Checkout Optimization Planning
- AI Wireframe Generator
Join Early Signup
If your team is building or redesigning search, join early signup and share your current zero-result rate. We will help you design a search experience that matches user intent more effectively.
Federated Search Design
When your product contains multiple content types that users search simultaneously, federated search combines results from different sources into one unified experience. Wireframe this with clear content type grouping and configurable priority ordering.
Design each content type group with: a section header showing content type and result count, type-specific result cards, and a "View all" link to filter by that type. The default view should show top two to three results per type, letting users identify which category contains their answer.
For products with large knowledge bases, wireframe assisted search that goes beyond autocomplete. Show contextual help panels alongside results: related FAQ entries, popular guides matching the query, and recently viewed items matching the search terms.
Wireframe the search analytics dashboard for product managers. This internal tool should show: most searched terms, zero-result query frequency, average clicks per search, and search-to-task completion correlation. This data drives iterative improvement of the search experience and content gap identification.