In addition to the importance of model performance, a key factor that must be considered is the quality of the data used in this work. The data set is limited in number of data points but, more importantly, in its chemical diversity. The deployment of these models must be carefully analyzed, and the applicability should be determined. The generalization study of the GNN-MG model suggests that the diversity of the dataset limits the model from being extrapolated to specific unknown conditions. To ensure optimal performance, the model should be used in accordance with the following recommendations. Temperature interpolation and extrapolation results suggest that the model has excellent accuracy. The isolated points also showed that the model prediction can have high deviations when new molecules or pressures are used. Predictions based on temperature within the range used for model training will have a good approximation of the solubility.