For comparison, alternative methods for generating atomic partial charges were tested on the same dataset. In addition to the classical EEQ and SQM methods -GFN1-xTB 2 and GFN2-xTB 3 -a hybrid physical/artificial neural network (ANN)-based approximation to AM1-BCC 18 charges, called EspalomaCharge, was evaluated. For this method, only neutral molecules were included, as specifying the formal charge was not possible. Additionally, molecules that encountered technical errors with EspalomaCharge were excluded from the comparison. Throughout the study, Hirshfeld 76 charges obtained from ωB97M- The correlation of atomic partial charges with the reference DFT charges is visualized in of CEH-v2 can be purely assigned to its more sophisticated construction. However, it should be noted that EEQ is faster by a factor of about ten in medium-sized systems as it requires only the solution of a linear system of equations in the number of atoms compared to the diagonalization in the number of AOs for CEH. The preceding version, CEH-v1, is only moderately less precise, which is unsurprising given that the objective of the revised version was to enhance robustness for challenging cases and expand applicability rather than improving the accuracy.