AlphaFold2 (AF2) provides structures for every protein, opening up prospects for virtually every field in structural biology. However, transmembrane proteins pose a challenge for experimental scientists, resulting in a limited number of solved structures. Consequently, algorithms trained on this finite training set also face difficulties. To address this issue, we recently launched the TmAlphaFold database, where predicted AlphaFold2 structures are embedded into the membrane plane and a quality assessment is provided for each prediction using geometrical evaluation. In this paper, we analyze how AF2 has changed the structural coverage of membrane proteins compared to earlier years when only experimental structures were available, and high-throughput structure prediction was greatly limited. We also evaluate how AF2 can be used to search for (distant) homologs in highly diverse protein families. By combining quality assessment and homology search, we can pinpoint protein families where AF2 accuracy is limited, and experimental approaches are still desired.