Vector Search Methods Comparison Simulation - By Pejman Ebrahimi

1. Exact Nearest Neighbor Search (ENN)

Finds the exact closest data points to a query by calculating distances to all vectors in the dataset.

Step 0: Data points
Initial dataset with vectors in feature space. The query point (red) will be compared against all data points.
Dataset Points
Query Point
Nearest Neighbor

Key Features:

  • 100% accuracy - finds the true nearest neighbors
  • Computationally expensive for large datasets (O(n) complexity)
  • Becomes inefficient in high dimensions (curse of dimensionality)
  • Simple implementation - just calculate all distances and sort
2. Approximate Nearest Neighbor Search (ANN)

Sacrifices perfect accuracy for speed by using efficient data structures to approximate nearest neighbors.

Step 0: Indexed structure
Data is pre-organized into efficient lookup structures that cluster or partition the vector space for faster searching.
Dataset Points
Query Point
Search Region
Returned Neighbors

Key Features:

  • Much faster than ENN for large datasets (sub-linear time complexity)
  • Trades accuracy for speed (95-99% accurate typically)
  • Requires pre-processing to build index structures
  • Various algorithms optimized for different use cases
3. Semantic Search

Uses meaning of content rather than keywords by searching through dense embedding vectors that capture semantic relationships.

Step 0: Text documents
Starting with raw text documents or queries before encoding into vector space.
Document Embeddings
Query Embedding
Semantic Matches

Key Features:

  • Understands meaning beyond exact keyword matches
  • Uses dense vector embeddings (typically 768-1536 dimensions)
  • Trained on large text corpora to capture language patterns
  • Effective for natural language, images, and multimodal content
  • Usually implemented with ANN algorithms for efficiency
4. Sparse Vector Search

Uses high-dimensional sparse vectors where most elements are zero, optimized for keyword and token matching.

Step 0: Tokenized content
Documents broken down into tokens (words/terms) before converting to sparse vector representation.
Vocabulary Dimensions
Query Terms
Matching Terms

Key Features:

  • Efficient for exact matching and keyword search
  • Very high dimensionality (vocabulary size) but mostly zeros
  • Uses specialized inverted index for quick lookup
  • Good for precision when exact matches are required
  • Often combined with semantic search for hybrid approaches

Comparison of Vector Search Methods

Feature Exact NN (ENN) Approximate NN (ANN) Semantic Search Sparse Vector Search
Accuracy 100% exact High (95-99%) Context dependent High for exact matches
Speed Slow (O(n)) Fast (sub-linear) Moderate to fast Very fast for keywords
Scalability Poor Good Good with ANN Excellent
Vector Type Dense or Sparse Usually Dense Dense Sparse
Use Cases Small datasets, high precision required Large-scale vector search, recommenders NLP, content discovery, similar item search Search engines, document retrieval
Common Metrics Euclidean, Manhattan, Cosine Euclidean, Inner Product, Cosine Cosine, Dot Product Jaccard, BM25, TF-IDF
Dimensions Any Moderate to high High (768-1536 typical) Very high (vocabulary size)
Example Tools SciPy, NumPy FAISS, Annoy, HNSW Pinecone, Weaviate, Milvus Elasticsearch, Lucene