We have therefore used the Class 2 xTB-ML-300k model to make quick, relatively accurate calculations for S 1 and T 1 energies, and have used the results to screen for potential sensitizers and emitters. This screening was relatively fast as the dataset size was small (250k) and xTB-sTDA results were already provided by Wilbraham et al. For larger datasets on the order of millions (PubChem) or billions of molecules (GDB-17) , running xTB-ML becomes expensive. A more intelligent sampling technique (such as active learning) could be used to screen such large databases, and this is an avenue of future work.