As an example of this analysis, the five best solvents are predicted for two moleculesmaleic anhydride (Fig. ) and oxindole (Fig. ). While only the five best solvents are shown, the reported RMSE values and the values of Kendall's Tau were obtained for the set of all organic solvents. The ranked solvents are shown for all thirty molecules in Fig. and the distribution of RMSE values and Kendall's Tau for those molecules are shown in Fig. . The analysis reveals that while the RMSE values can be non-ideal, the model is able to predict the solubility trends across solvents well, with most of the predictions yielding a Kendall's Tau lower than 0.3 and only one prediction slightly exceeding 0.5. This metric highlights the ability of the model to rank the solvents from best to worst and demonstrates that such models are capable of qualitatively screening organic solvents, making them applicable to identifying good organic solvents. Maleic anhydride is an example where the model ranked the solvents well (Kendall's Tau of 0.179), but the solubility predictions themselves are not ideal (RMSE of 0.737 log(x)). Oxindole, on the other hand, is an example where the model performed worse at ranking the solvents (Kendall's Tau of 0.364), but the predicted solubility values were closer to the actual values (RMSE of 0.364 log(x)).