Recent trends in structural biology emphasize the importance of collecting multiple sets of redundant data, especially for systems with low signal-to-noise ratios. This has significant implications for MicroED. Firstly, for complex systems where only a fraction of a sample diffracts to the desired resolution, a major part of the data collection process is dedicated to finding the best crystals. Secondly, redundant data can improve the signal-to-noise ratio and reduce the impact of systematic artifacts during data collection . Finally, merging datasets to increase completeness is often necessary, but the outcome of this approach depends on the isomorphism of the data sets .In particular, since phasing using ab initio methods depends entirely of the structure factors, reliable estimates are required. A large pool of data sets facilitates the prospect of finding isomorphous data sets that could then be merged together to produce a complete data set for structure determination. This approach was demonstrated here with Mixture C where despite the use of a short rotation range, all structures in mixture C were successfully solved as a step toward future high throughput MicroED structure determination.