) can be calculated from Eq. 2. The solvation free energy at 298K in a variety of organic solvents is a property that can be predicted with a deep neural network because of data availability. Recently, we published two databases, CombiSolv-QM and CombiSolv-Exp, with 1 million quantum chemical (COSMO-RS) and 11 thousand experimental datapoints respectively for the solvation free energy in close to 300 solvents. A transfer learning method was employed where the deep neural network was pre-trained on quantum chemical data and fine-tuned with experimental data. The final model can predict solvation free energies at 298K with a RMSE/MAE lower than 0.44/0.21 kcal/mol. The transfer learning approach improves the performance on higher molar mass solutes compared to direct training of the deep neural network on experimental data. With the published transfer learning model, ∆G solv,298K (Eq. 1) can be predicted, and K X 1 , K X 2 , and P dry,X 1 -X 2 (Eq. 2) can be calculated.