Indexing very large and high-dimensional data in resource-limited settings is a challenging task. While sketching techniques can reduce the data size, the resulting sketches – if useful for indexing – are often (almost) uncorrelated. That makes it hard to build an index structure for the sketches. In this work, we propose to group the bits of the sketches and use these groups as hash values. We derive expected recall values and show that the recall of the index can be controlled by the number of groups and bits per group. The parameter choice is a trade-off between recall, speed, and memory consumption. In a practical setting, the approach is combined with Stochastic HIOB sketches and achieves a speedup of near 100 times over a linear scan of the sketches.