Abstract. In modern database applications the similarity of complex objects is examined by performing distance-based queries (e.g. nearest neighbour search) on data of high dimensionality. Most multidimensional indexing methods have failed to efficiently support these queries in arbitrary high-dimensional datasets (due to the dimensionality curse). Similarity join queries and K closest pairs queries are the most representative distance join queries, where two highdimensional datasets are combined. These queries are very expensive in terms of response time and I/O activity in case of high-dimensional spaces. On the other hand, the filtering-based approach, as applied by the VA-file, has turned out to be a very promising alternative for nearest neighbour search. In general, the filtering-based approach represents vectors as compact approximations, whereas by first scanning these approximations, only a small fraction of the real vectors is visited. Here, we elaborate on VA-files and develop VA-file based algorithms for answering similarity join and K closest pairs queries on highdimensional data. Also, performance-wise we compare the use of VA-files and R*-trees (a structure that has been proven to be of robust nature) for answering these queries. The results of the comparison do not lead to a clear winner.