---
title: Search with Qdrant
description: Qdrant enhances search, offering semantic, similarity, multimodal, and hybrid search capabilities for accurate, user-centric results, serving applications in different industries like e-commerce to healthcare.
features:
- id: 0
  icon: 
    src: /icons/outline/similarity-blue.svg
    alt: Similarity
  title: Semantic Search
  description: Qdrant optimizes similarity search, identifying the closest database items to any query vector for applications like recommendation systems, RAG and image retrieval, enhancing accuracy and user experience.
  link:
    text: Learn More
    url: /documentation/concepts/search/
- id: 1
  icon:
    src: /icons/outline/search-text-blue.svg
    alt: Search text
  title: Hybrid Search for Text
  description: By combining dense vector embeddings with sparse vectors e.g. BM25, Qdrant powers semantic search to deliver context-aware results, transcending traditional keyword search by understanding the deeper meaning of data.
  link:
    text: Learn More
    url: /documentation/beginner-tutorials/hybrid-search-fastembed/
- id: 2
  icon:
    src: /icons/outline/selection-blue.svg
    alt: Selection
  title: Multimodal Search
  description: Qdrant's capability extends to multi-modal search, indexing and retrieving various data forms (text, images, audio) once vectorized, facilitating a comprehensive search experience.
  link:
    text: View Tutorial
    url: /documentation/tutorials/multimodal-search-fastembed/
- id: 3
  icon:
    src: /icons/outline/filter-blue.svg
    alt: Filter
  title: Single Stage filtering That Works
  description: Qdrant enhances search speeds and control and context understanding through filtering on any nested entry in our payload. Unique architecture allows Qdrant to avoid expensive pre-filtering and post-filtering stages, making search faster and accurate.
  link:
    text: Learn More
    url: /articles/filtrable-hnsw/
sitemapExclude: true
---