Here, we combine advances in cheminformatics software and a recently compiled database of organic solubility, BigSolDB, and develop a new state-of-the-art general organic solubility prediction model and validate it under rigorous extrapolation conditions. By adapting the fastprop and chemprop architecures to ingest two molecular structures and a temperature, we can train models on BigSolDB to regress logS directly. Our optimized models yield a factor of three improvement over the existing state-of-the-art organic solubility prediction models, with rapid inference times suitable for use in high-throughput workflows. We further demonstrate that our optimized fastprop and chemprop models, which use fundamentally different molecular representations, both reach the irreducible error, or aleatoric limit, of accuracy with only a small fraction of the total available data. This indicates that further improvements on organic solubility predictions must be achieved through higher quality datasets rather than larger datasets or more expressive models. Our fastest model, termed fastsolv, is open source and can be downloaded as a python package, accessed online via fastsolv.mit.edu, and is integrated in ASKCOS (askcos.mit.edu) and Reaction Mechanism Generator (RMG, rmg.mit.edu). Fig. Machine learning representation of solutions. (a) In our modeling approach, solute and solvent structures are mapped to feature vectors. These feature vectors are concatenated to the solution temperature to arrive at a solution representation, which is passed into a fully-connected neural network and regressed to the log S. (b) Structures are mapped to feature vectors using a fixed representation of Mordred descriptors as implemented in fastprop, or a learned representation derived from message passing on a graph representation from chemprop. We compare the performance of models trained on these fundamentally different solution representations.