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The vector space model represents documents and queries as weighted term vectors in high-dimensional space, enabling similarity computation via cosine measures rather than Boolean exact-match retrieval. Experiments on the SMART system demonstrate that term weighting schemes combining term frequency (TF) with inverse document frequency (IDF) consistently outperform binary indexing, with IDF-weighted vectors producing superior recall-precision tradeoffs across multiple test collections. This mechanism directly powers ranked retrieval systems by scoring documents against queries through continuous similarity values, replacing brittle keyword matching with a scalable, corpus-aware relevance signal that underlies modern inverted index scoring functions including BM25 and learning-to-rank feature generation.