Here, we consider LGBM and RF model architectures as potential regressors to predict aqueous solubility and organic solubility for the AqSolDB and BigSolDB datasets, respectively. Our model selection is justified both by literature reports on solubility predictions and by the fact that treebased models often outperform deep learning models on medium-sized tabular datasets, which both BigSolDB and AqSolDB are. An LGBM trained on a dataset that was merged from organic and aqueous solubility datasets (17,536 datapoints) was demonstrated to predict solubility just as well as graph convolutional neural networks. Furthermore, Ye and Ouyang reported that an