In conclusion, we present organic solubility prediction models using deep learning on fixed and learned molecular representations, then test them under rigorous solute extrapolation. Our models outperform comparable literature models by a factor of 2-3 on publicly-available test sets. We demonstrate that our models are predicting near the aleatoric limit of the experimental training data, motivating the assembly of highly accurate datasets for the field to notice further improvements in organic solubility prediction. Given the importance of solid solubility prediction, we have termed the fixed representation model fastsolv and taken extensive steps to make it available. fastsolv can be accessed via any web browser for free at fastsolv.mit.edu, downloaded as a python package for use in scripting, and is integrated directly within the ASKCOS (askcos.mit.edu) and RMG (rmg.mit.edu) platforms for retrosynthesis and reaction mechanism generation, respectively.