The xtb-GNF/MachineLearning/CORINA MD, RESP/GAFF/MMPBSA-Cor, and AMOEBA/DDM/BAR methods have greater prediction errors for systems with negatively charged guests, which could potentially relate to the challenges alchemical methods have with charged guests (Section 3.1). Both xtb-GNF/MachineLearning/CORINA MD and RESP/GAFF/MMPBSA-Cor use the GAFF energy model, and its combination with explicit fixed charge water models typically results in predicted free energies that are too favorable (particularly, prior work has shown that GAFF with TIP3P leads to a consistent error in this direction for guests containing carboxylates and alcohols ). This is exactly the case here for systems with guests containing a carboxylate for the xtb-GNF/MachineLearning/CORINA MD method, where an AM1-BCC charging scheme, explicit TIP3P water, and GAFF energy model is used. The RESP/GAFF/MMPBSA-Cor method also used the GAFF energy model, but with implicit (PB/SA) water and a RESP charging scheme. During the SAMPL7 virtual workshop the RESP/GAFF/MMPBSA-Cor participants noted that in their methodology comparison of RESP and AM1-BCC charging schemes, RESP resulted in better accuracy; it would be interesting to know if this holds true more generally. For the AMOEBA/DDM/BAR method, the single outlier was exoOA-g4, with a ΔG prediction error of 2.5 kcal/mol.