High quality datasets are an increasingly important resource in the chemical community as the use of ML models is growing. To ensure the quality and comparability of dataset entries, all structures should be optimized by the same electronic structure method. This step is crucial if the source of an entry uses 1D representations such as SMILES, 105,106 SELFIES, 107 or other, sometimes graph-based, specifications 108 to store chemical information. Semiempirical electronic structure methods are a natural choice for this application since numerous calculations need to be executed when curating a dataset. The robustness and efficiency of xTB methods made them one of the methods of choice for this purpose. Using those elaborate datasets, models can be built, that describe a correlation between an input vector, also referred to as "labels", and a target property. During the training phase, a statistical model is optimized to minimize the loss function for predicting the target based on the labels. The complexity of these labels can vary drastically from basic properties like the molecular weight of a molecule to electronic properties such as the Hamiltonian matrix or orbital energies. The labels should represent the necessary chemical information of a molecule to predict the target property. The computation of such labels should be efficient since they are computed not only during the training phase but also during the prediction or inference phase. As discussed in the previous paragraph, xTB methods are commonly used for structure optimizations when generating datasets. The use of labels based on xTB properties is therefore associated with no additional computational cost.