Extensive efforts were made to identify a physics-informed neural network architecture which would infuse inductive bias into the network and improve predictions. Each additional facet was enabled during the automated hyperparameter optimization such that the algorithm would automatically deduce which was the most effective. To allow the network to learn a unique per-solvent and per-solute representation rather than a single 'solution' representation, distinct linear layers were added after the initial Mapping. This reflects the intuitive understanding that each solute and solvent should have a unique contribution to the resulting solubility which is independent of the exact solution. The manner in which the latent solute and solvent representations was also configurable -in analogy to existing solubility prediction models, the network could choose to perform element-wise multiplication, subtraction, or addition instead of simple concatenation. Across many repeated hyperparameter optimization instances none of the physics-infused models were able to outperform the simple architecture presented in the main text. Inductive bias was unable to surpass reliance on universal approximator theorem, at least given the current aleatoric limit of available data.