ML models are typically evaluated based on their ability to reproduce reference calculations and are then, if applicable, compared against experimental results. While this validation strategy has been used successfully in many recent studies , including our own , the comparison of QM-GNNIS against the computational ground truth (i.e., explicit-solvent ab initio MD or QM/MM MD) is too expensive for the system sizes, simulation lengths, and level of theory we are using. For this reason, we rely on experimental data to evaluate the performance of the approach. To rigorously study the proposed methodology, a large number of ∼200, experimental measurements of 24 different test systems are studied. As a point of reference, QM calculations with the state-of-the-art implicit solvent models SMD and openCOSMO-RS are compared to the proposed approach. Note that we chose SMD solvent (and not CPCM) for the comparison as it was fitted to better reproduce solvation free energies of solutes in different solvents and can, hence, be thought of as an improvement over CPCM . This fitting is achieved by using solvent-specific parameters that should better describe solvent characteristics (e.g., hydrogen bonding) not captured by the dielectric permittivity of the solvent. As this procedure may already capture some of the explicit solvent effect, we anticipated that the combination of SMD and QM-GNNIS may overestimate some interactions. Therefore, we decided to combine QM-GNNIS with the simpler CPCM model.