With the proliferation of multimedia data, there is an increasing need to support the indexing and searching of high-dimensional data. In this paper, we propose an efficient indexing method for high-dimensional multimedia databases using the filtering approach, known also as vector approximation approach which supports the nearest neighbor search efficiently. Our technique called RA+-Blocks (Region Approximation Blocks) divides a high-dimensional feature vector space into compact and disjoined regions. Each region will be approximated by two bit-strings according to the RA-Blocks technique. RA+-Blocks improves the division strategy of data space compared to the RA-Blocks. From our experiment using high-dimensional feature vectors, we show that RA+-Blocks achieves better performance on the nearest neighbor search than VA-File and RA-Blocks on both uniform and real data.