The training metrics for the RF were better than the baseline model using the MACCS Keys of both molecules as inputs. However, the test results show increased prediction errors considering all the statistical metrics. The RF test performance is not as good as that of the ANN counterpart. Nevertheless, molecular fingerprints are simple representations of DES mixtures that do not require too much effort to calculate and provide a competitive model. This implies that models based on complex representations of DES may not be necessary when a molecular fingerprint correlates well with solubility. Therefore, models should be deployed first using the most straightforward data representations before generating more complex architectures. This observation is confirmed with the GCN, which is a more complex model that performs worse than the RF regressor and the baseline model. The GCN had high deviations in the results shown in Table . This is expected, as the number of parameters increases compared to the RF and ANN models. Additionally, the incorporation of polarity and polarizability characteristics with hydrogen bond information into the GCN may reduce those high deviations and improve the accuracy of the final model.