To realize the full potential of ML model prediction accuracy and generalizability, the hurdle of data volume and quality must be addressed. As mentioned earlier, experimental automation offers a direct path to increasing throughput and consequently the volume of data. This data is expected to be high-quality but may not be directly applicable depending on the tasks at hand (i.e., possible limitations on what can be automated). Therefore, ML solutions that can tackle limited data are appealing, and their proven success in chemistry applications offers guidance for the pharmaceutical formulation space . Wiercioch and Kirchmair recently described an approach to handling a lack of data for the prediction of aqueous solubility of small molecules (Figure ) . In this case, a deep learning architecture consisting of an encoder-decoder design was applied on a pKa dataset based on molecular structures (i.e., SMILES: simplified molecular input line entry system) for 6245 compounds. Then, transfer learning was used to predict solubility for a smaller, non-overlapping dataset (i.e., 1311 molecules) based on the pretrained pKa model. In essence, the important features of the dataset were used to represent the molecules and predict pKa, and this knowledge was then transferred to predict aqueous solubility with a high degree of accuracy . The main takeaway with this approach is that in cases where datasets for the property of interest are not readily available, one can implement a ML strategy using more abundant data on a closely related proxy predictive task. In the case of pharmaceutical formulation research, where large datasets are not readily available, we anticipate this transfer learning approach will be beneficial.