Many methods exist for the prediction of the solubility of solids in specific organic solvents. A recently published review by Kuentz et al. summarizes recent advances in solubility predictions. The main limitation of existing methods is that they require physical properties of the solute, such as the fusion enthalpy and/or melting temperature (e.g. the well-known NRTL method ) or empirical solute parameters (e.g. in the relationships developed by Abraham et al. ). Other methods for the direct prediction of solid solubility are always limited to one or a few solvents. For example, the aqueous solid solubility is important in many applications, including the initial screening of potential API's, and thus there are enough data available to train deep neural networks for the prediction of this property. Some examples of recent successful data-driven predictors for the aqueous solid solubility are AquaSol developed by Lusci et al., the models of Cui et al. and by Boobier et al., and SolTranNet developed by Francoeur and Koes. Despite of the tremendous progress made in machine learning for molecular and material science, for the direct prediction of solid solubility in organic solvents, the application of data driven methods is limited by data scarcity. Many have applied machine learning for the prediction of properties related to the solubility of gaseous solutes in organic solvents and water. Recently, one attempt has been made by Boobier et al. to also use machine learning for the direct prediction of solid solubility in organic solvents using experimental data and computational descriptors. Machine learning models were built for the prediction of the solid solubility in water, ethanol, benzene, and acetone. For each of those solvents, a separate dataset was built and a different machine learning model was trained. While a good performance was achieved, data availability limits the application of this method to a narrow range of solvents and temperatures. Because solid solubility data at elevated temperatures are even more scarce, thermodynamic models such as NRTL, UNIFAC, and UNIQUAC are typically used to predict the temperature dependence of solubility; nonetheless, these models are limited by the availability of empirical parameters. The physics based computation of solubility has significant potential as demonstrated by, for example, Fowles et al. for the aqueous solid solubility. However, the large computational cost is a limiting factor for the fast screening of the solid solubility in different solvents.