Understanding how solvation affects structure-property and property-property relationships of drug-like molecules is crucial for de novo design, as most relevant reactions occur in aqueous environments. We have thus performed an exhaustive analysis of the recently proposed Aquamarine dataset to gain insights into the effect of solvent-molecule interaction on the quantum-mechanical (QM) properties of large drug-like molecules. Our results show that the inclusion of an implicit solvent model of water changes the values of (extensive and intensive) QM properties but it does not alter the correlations among them. Moreover, we have found that solvation can limit the identification of unique molecular conformations, with variations in specific properties being rationalized by the extent of structural changes. \(\varDelta \) -learning approach was used to predict solvent effects on the dipole moment \(\mu \) and the many-body dispersion energy \(E_\textrm{MBD}\) , resulting in more accurate and scalable predictive models compared to these directly trained on solvated properties. Hence, our work provides valuable insights into the effect of solvent-molecule interaction on physicochemical properties, which could assist in the development of machine-learning models for designing solvated molecules of pharmaceutical and biological relevance.