Effectiveness and efficiency are two important concerns in using multimedia descriptors to search and access database items. Both are affected by the dimensionality of the descriptors. While higher dimensionality generally increases effectiveness, it drastically reduces efficiency of storage and searching. With regard to effectiveness, rele-vance feedback is known to be a useful tool to squeeze in-formation from a descriptor. However, not much has been done toward enabling relevance feedback computation us-ing high-dimensional descriptors over a large multimedia dataset. In this context, we have developed new methods that enable us to a) reduce the dimensionality of Gabor tex-ture descriptors without losing on effectiveness, and b) per-form fast nearest neighbor search based on the information available during each iteration of a relevance feedback step. Experimental results are presented on real datasets.