Furthermore, rows 3 and 4 in Table correspond to reference data generated using optimized geometries obtained with GFN2-xTB, combined with single point energies used to refine the potential energy surface, using either ANI2x or QM at the B3LYP-D3BJ/DZVP level, respectively. We can see that accuracy gains are possible using this flexible, hybrid approach for reference data generation. In particular, for the B3LYP-D3BJ/DZVP//GFN2-xTB method, the RMSE is significantly reduced from 1.1 to 0.6 kcal/mol, with a total computational cost at a fraction of the full QM torsion drive (relative computational costs of all of these methods have been reported elsewhere ). All of the BespokeFit augmented FFs tested also show a decrease in the maximum RMSD between the QM reference and MM optimized geometries, despite only a small proportion of the total valence terms of each molecule being optimized. The methods shown in Table represent a hierarchy of increasing accuracy as the reference data generation method becomes more expensive. Thus, users are able to optimize the balance between fitting time and accuracy to suit their needs all through a common interface between BespokeFit and QCEngine.