A major problem within peer-to-peer systems is to find the best peer given a certain query. Inspired by the work in the area of social networks we present a novel peer-to-peer system called INGA (Interest-based Node Grouping Architecture). Peers cooperate to efficiently route queries along adaptive shortcuts based overlays using only local knowledge. We propose active and passive shortcut creation strategies and a new routing algorithm that combines a greedy, high degree and flooding based search depending on one's knowledge. We quantify the benefit of the overlay network by comparing the performance of INGA in the SWAP simulation infrastructure against a simple Gnutella style network, against recently proposed shortcut networks without similarity metrics and against a relaxation based approach. While obtaining the same recall we show in our experiments that with INGA we half the messages for a query.