In this work, we propose a graph representation for DESs that incorporates explicit information on the molecular structure of its components and of their intermolecular interactions (e.g., hydrogen bonding).We use this representation for training a GNN based on available data from the literature. We explore the predictive performance of the proposed GNN at different temperatures, pressures, and molar ratios of the DES mixture. In addition, HBA and HBD molecules are excluded from the dataset to assess the GNN performance on predicting DES with molecules not seen during the training phase. Moreover, the applicability domain of the GNN and some aspects of the model high-level interpretability in predictions are discussed. We benchmarked the proposed framework with other representations (sigma profiles, molecular fingerprints, and graphs ) and models ((artificial neural networks, random forests, graph convolution networks) reported in the literature . In Figure we summarize the DES representations and models explored. We show that GNNs and mixturegraph representations offer superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous literature). This result is important, as obtaining sigma profiles requires expensive computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature conditions reliably, achieving mean absolute errors below 0.1; moreover, when extrapolating to other pressures, the model can accurately predict points within ±20% of the pressure range used for training. However, we found deficiencies in the ability of the model to predict solubility for new DES mixtures; to explain this behavior, we studied the chemical diversity of available datasets and found this to be rather constrained. Our models, data, and benchmarks are shared as Jupyter notebooks, which are available at . The proposed computational capabilities can help navigate the design space of DES, inform data collection efforts, facilitate benchmarking, and develop educational resources.