We propose a strategy to perform query processing on P2P similarity search systems based on peers and super-peers. We show that by approximating global but resumed information about the indexed data in each peer, the average amount of computation and communication performed to solve range queries can be significantly reduced as compared to alternative state of the art strategies based on local indexing at peer level. We illustrate our technique by using an indexing method based on compact clustering.