A possible cause for the deviation of the prediction and the experimental results, aside from potential inaccuracies in the model and visual inspection error in experiments, comes from the non-idealities of mixing. A common method to estimate the solubility of a species in a mixture of two solvents is to use a weighted average of the solubility in either individual solvent, such that the solubility in a mixture of any composition of co-solvents is a linear extrapolation between the two pure solvent's solubilities. However, in many instances, this will not be the case and solubilities in the mixtures may either be higher or lower than that predicted by the weighted average. We also note that the model was trained primarily on the solubility of solid organic compounds, while both benzaldehyde and limonene are liquid organic compounds. Not only does the experimental methodology for determining solubility differ between solids and liquids, but the nature of their solubility also varies. In contrast to solid solubility, the solute phase in liquids often contains diffused solvent as well. Also, the observations regarding the solubility of benzaldehyde in acetic acid/water and DMA/water mixtures highlight the non-linear relationship between the volume percentage of the co-solvent and the solubility. The non-linear behavior of the solubility of limonene in the DMA/water mixture provides additional evidence of the limitations of our approach. Additionally, even though the organic solubility model was shown to perform well in terms of distinguishing between good and poor solvents, it is important to acknowledge that, while having most of the Kendall's Tau values below 0.3 indicates good screening abilities, it does not mean that the model is able to perfectly predict the rank of the solvent, as that would correspond to Kendall's Tau values closer to zero.