Multi-hop retrieval follows chains of reasoning across multiple document retrievals to answer complex questions. Problem: Some questions require information from multiple documents that must be connected logically. "Who founded the company that made the device used in the Apollo missions?" Mechanism: First retrieval answers partial question → extract entities/facts → formulate follow-up query → retrieve again → chain until complete. Approaches: Iterative: Retrieve → reason → retrieve again based on findings. Query decomposition: Break complex query into sub-queries, retrieve for each, synthesize. Agentic: Agent decides when more retrieval needed and what to retrieve. Example flow: Q: "CEO of company that acquired Twitter" → retrieve "Elon Musk acquired Twitter" → retrieve "Elon Musk is CEO of Tesla, SpaceX" → answer. Challenges: Error accumulation across hops, determining when to stop, increased latency. Evaluation: Multi-hop QA benchmarks (HotpotQA, MuSiQue). Frameworks: LangChain multi-hop retrievers, custom agent loops. Optimization: Cache intermediate results, limit hop depth, verify reasoning chain. Essential for complex reasoning over knowledge bases.