Web search over peer-to-peer (P2P) networks shows promise to become an alternative to the state-of-the-art search engines since P2P overlays offer means for decentralized search across widely-distributed document collections. However, the design of effective techniques for P2P indexing and retrieval raises a number of technical challenges due to potentially unscalable resource (e.g. bandwidth, storage) consumption. The paper presents a framework for full-text information retrieval in structured P2P networks and introduces a novel retrieval model based on highly discriminative keys—terms and term sets appearing in a restricted number of documents—that ensure efficient and scalable retrieval. Our goal is to design scalable techniques for building a global key index in structured P2P overlays for large document collections. We present experimental results that show acceptable indexing and retrieval costs while the retrieval quality is comparable to standard centralized solutions with BM25 relevance computation scheme.