Due to the importance of aqueous solubilities in different industrial processes and environmental applications, a scientific challenge consisting of the prediction of 32 solubilities given a database of 100 reliable measurements was created with the goal of comparing the outcomes of different solubility prediction techniques. Participants employed methods such as artificial neural networks , quantitative structure-property relationship (QSPR) , and deep learning to predict the aqueous solubilities of drug-like molecules. All of the employed methods were empirical and trained on existing measurements. The limitation of these methods, however, is the dependence of a training set of data that limits their applicability to compounds similar to those in the training of the set and impairs its transferability. Some newer methods attempt to predict solubilities based on a physical description of the interactions in solution and in the solid state, yielding results that are in principle rigorous given an accurate energy model and an adequate method. In these approaches, molecular systems are described by force fields, i.e, potential energy functions that contain parameters describing bonds, atoms, electrostatic and non-electrostatic interactions. Molecular dynamics (MD) or Monte Carlo (MC) simulations are commonly used to sample different configurations of the system described by an energy model called a force field. The simulations then allow the estimation of physical properties such as internal energy, free energy, and enthalpy under different conditions. The quality of the results of such methods depends on how well the force field describes the system under study and how good the sampling method is. Thus, some researchers have recently estimated aqueous solubilities using simulations of thermodynamic cycles encompassing the crystal, the ideal gas, and an infinitely dilute solution of a given molecule . When the structure of the solid is unknown, some studies have substituted simulations of solid melts in place of a structure of the solid .