From the GNN approaches tested, the GNN-MG model exhibited the best performance, considering the MAPE as a selection criterion. Compared with the GCN, a best fit was achieved when each graph node, edge, and global features were updated and passed through the model. The mixturegraph approach also provided the best representation of the DES mixture, where hydrogen bond information can be used to replace complex representations such as the sigma profiles. We found that a simple representation of DES based on molecular fingerprints combined with an RF model had the worst performance, but that it was still satisfactory. This indicates that simple models might be sufficient to conduct quick screening of DES formulations.