Relevance feedback is often used in refining similarity retriev-als in image and video databases. Typically this involves modifi-cation to the similarity metrics based on the user feedback and recomputing a set of nearest neighbors using the modified similar-ity values. Such nearest neighbor computations are expensive given that typical image features, such as color and texture, are represented in high dimensional spaces. Search complexity is a critical issue while dealing with large databases and this issue has not received much attention in relevance feedback research. Most of the current methods report results on very small data sets, of the order of few thousand items, where a sequential (and hence exhaustive search) is practical. The main contribution of this paper is a novel algorithm for adaptive nearest neighbor compu-tations for high dimensional feature vectors and when the number of items in the database is large. The proposed method exploits the correlations between two consecutive nearest neighbor searches when the underlying similarity metric is changing, and filters out a significant number of candidates in a two stage search and retrieval process, thus reducing the number of I/O accesses to the database. Detailed experimental results are provided using a set of about 700,000 images. Comparison to the existing method shows an order of magnitude overall improvement.