Most proteins found in the outer membrane of gram-negative bacteria share a common domain: the transmembrane β-barrel. These outer membrane β-barrels (OMBBs) occur in multiple sizes and different families with a wide range of functions evolved independently by amplification from a pool of homologous ancestral ββ-hairpins. This is part of the reason why predicting their three-dimensional (3D) structure, especially by homology modeling, is a major challenge. Recently, DeepMind's AlphaFold v2 (AF2) became the first structure prediction method to reach close-to-experimental atomic accuracy in CASP even for difficult targets. However, membrane proteins, especially OMBBs, were not abundant during their training, raising the question of how accurate the predictions are for these families. In this study, we assessed the performance of AF2 in the prediction of OMBBs and OMBB-like folds of various topologies using an in-house-developed tool for the analysis of OMBB 3D structures, and barrOs. In agreement with previous studies on other membrane protein classes, our results indicate that AF2 predicts transmembrane β-barrel structures at high accuracy independently of the use of templates, even for novel topologies absent from the training set. These results provide confidence on the models generated by AF2 and open the door to the structural elucidation of novel transmembrane β-barrel topologies identified in high-throughput OMBB annotation studies or designed de novo.