An unstructured peer network application was proposed to address the query forwarding problem of distributed search engines and scalability limitations of centralized search engines. Here we present novel techniques to improve local adaptive routing, showing they perform significantly better than a simple learning scheme driven by query response interactions among neighbors. We validate prototypes of our peer network application via simulations with 500 model users based on actual Web crawls. We finally compare the quality of the results with those obtained by centralized search engines, suggesting that our application can draw advantages from the context and coverage of the peer collective.