Learning what a specific user is exactly looking for, during a session of image search and retrieval, is a problem that has been mainly approached with "classification" or "exploration" techniques. Classification techniques follow the assumption that the images in the archive are statically subdivided into classes. Exploration approaches, on the other hand, are more focused on following the varying needs of the user. It turns out that image retrieval techniques based on classification approaches, though often showing good performances, are not prone to adapt to different users' goals. In this paper we propose a relevance feedback mechanism that drives the search into promising regions of the feature space according to the Nearest Neighbor paradigm. In particular, each image labelled as being relevant by the user, is used as a "seed" for an exploration of the space based on the Nearest Neighbors paradigm. Reported results show that this technique allows attaining higher recall and average precision performances than other state-of-the-art relevance feedback approaches.