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 needs 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.