Methods have evolved from empirical group additivity correlations, , to ab initio conductor-like screening model (COSMSO) and its extension to realistic solvation (COSMO-RS), to bespoke-solvent machine learning (ML) models with random forest regressors. Given the specific importance of aqueous solubility in drug discovery, most effort has focused on predicting aqueous solubility, and relatively fewer works have explored organic solvents, which are particularly crucial in synthetic processes. The aforementioned experimental variability limit of 0.5-1 log S represents a bound on the performance of any data-driven prediction method, since this variability is irreducible. This variability limit has primarily been explored in the context of aqueous solubility in today's literature. However, there is little reason to believe that the experimental uncertainty should be any lower in organic solvents, and may actually be higher due to increased variability in the experimental methodologies used across laboratories. State-of-the-art methods focus on applying deep learning to organic solubility prediction, including graph-based neural networks and descriptor-based models . Existing models, however, suffer from a lack of generalizability for a variety of reasons. Boobier et al. trained solvent-specific models on only commonly available solvents at room temperature due to a lack of sufficient data to do otherwise, thus rendering the model non-generalizable by construction. Other works like those of Ye and Ouyang and Lee et al. fail to evaluate model performance when extrapolating to new, unseen solutes, a task which mirrors the real task where solubility prediction would be applied in a synthetic pipeline. The state-of-the-art model in literature by Vermeire et al. overcame some of these limitations by training a composition of deep learning models on compiled thermochemical data and using a thermocycle to predict solubility in arbitrary solvents for a wide range of temperatures. In this case, though, data was leaked from training to testing, resulting in overly optimistic performance reports as we demonstrate in this work. Reliance on a collection of machine learning models makes inference times with this model relatively slow, as well.