The labels computed from xTB calculations can be sorted into a few categories. First, a distinction between scalar and tensorial descriptors can be made. For the latter type, we can mainly find work by the Miller group. They published a series of neural network models based on quantum chemical tensors and named them OrbNet. Apart from this family of models, Wang et al. published a generative model predicting the 3D structure of drug-like molecules based on experimental and quantum-chemically computed electron densities. In terms of scalar properties, the HOMO-LUMO gap is a popular choice for applications involving electronic excitations. In other applications, the models are trained to maximize this gap. Besides the HOMO-LUMO gap, HOMO, LUMO and SOMO orbital energies are used as well. In contrast to the molecular properties presented to this point, atomic properties are also used by models, most commonly atomic partial charges. Usually, only the partial charge of atoms selected by chemical intuition is used, i.e., reaction or coordination centers.