While transfer learning (i.e., taking a ML model trained on one task as a starting point for another) may allow for a reduction in training data, acquiring a diverse training set would still require a substantial computational effort. In addition, a specific functional and basis set would need to be chosen, potentially limiting the approach's applicability to these specific choices. For this reason, we chose to develop an approach that extracts knowledge from using GNNIS in classical simulations, which we found to reproduce solute-solvent interactions well , and transfers it to QM calculations as a correction. This procedure has the crucial benefit that no further training data is required, and the approach should be applicable to any functional and basis set combination.