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" descrip- Besides organic molecules, xTB methods are also used in the structure optimization of the tmQM 112 dataset containing 86,665 mononuclear transition metal complexes. For this set, no xTB based labels are published. However, it shows the robustness of these methods beyond organic chemistry. This point is further underlined by smaller databases capturing specific parts of the chemical space: organocatalysts in OSCAR, 113 or aggregate materials in ASBase. In Fig. , the size of the datasets featured in this work is compared against the size of QM9 115 as a well-cited reference.