Since there is a vast number of examples where xTB methods are used in dataset generation tasks, we will only highlight those that put special emphasis on the use of xTB. One of the largest datasets for small-to mediumsized organic molecules is GEOM. 17 It constitutes a high quality data set collected from various sources with diverse experimental properties as labels. Using CREST and associated workflows, conformer sets for 317,928 molecules were computed at the GFN2-xTB level of theory. For a smaller portion of molecules, the level of theory was extended to DFT quality. GEOM thus constitutes a dataset to train models to predict conformer ensembles, 110 or to use conformer ensembles as input as compared to single structures. Another still growing dataset with a similar scope of molecules and an even greater collection of xTB-based labels is QMugs. Here, the conformational space of entries is also explored and included. The recent 1.1 update includes further steric and "dispersion" descriptors based on xTB.