The ability of a molecule to dissolve in a particular solvent is a key factor in determining whether it will be suitable for many technological applications, such as drug molecules that must dissolve in the bloodstream or redox-active molecules that must remain in solution inside a flow battery. Chemists assess the solubility of a molecule by computing the solvation energy (∆G solv ) -a measure of the strength of interaction between a molecule and a solvent -from quantum mechanical simulations. While methods for computing solvation energy are well established, 2 their computational expense is large enough (minutes to hours per compound on a single processor) to impose serious limitations on how many molecules can be assessed. Evaluating thousands of chemicals requires significant computational resources, yet would explore a small fraction of modern chemical search spaces that approach billions of molecules. Faster approaches, such as machine-learned QSAR models, are required to reach the necessary evaluation throughput to scan for promising leads in these large chemical spaces.