Recently, machine learning (ML)-based approaches have emerged as a particularly promising direction for solubility prediction, as evidenced by a growing body of literature dedicated to this topic. This study does not consider literature ML approaches based on limited datasets, which typically include only a few compounds. Such models are generally unsuitable for predicting the solubility of novel solutes. However, there have been notable exceptions. For instance, studies have employed substantially larger datasets, encompassing about a hundred compounds, to develop predictive models.Nevertheless, these studies have employed random data splitting, resulting in an overly optimistic estimation of prediction error. In contrast, our recent study employed a dataset comprising 187 substances, where a rigorous data split revealed a substantially higher prediction error compared to random splitting. However, this dataset remains insufficient for achieving robust generalization.