The final ingredient of the utility of our model is the speed at which we can evaluate new molecules. Evaluating the model requires parsing the SMILES string to an RDKit molecule object, converting the RDKit molecule to a graph representation compatible with Tensorflow, and evaluating the MPNN. The conversion steps can be performed in parallel for each molecule and the individual mathematical operations of the MPNN can also be parallelized, allowing us to exploit highly-parallel CPUs and general-purpose GPUs. Accordingly, we can achieve an evaluation rate of 10 3 molecules per second on two 32-core Intel Xeon CPU E5-2683 v4 CPUs. This evaluation rate makes it possible to evaluate all of QM9 (10 5 molecules) in two minutes and all of ZINC15 in 33 hours using only a single workstation. The extreme evaluation rates noted here can be further improved by running the models on multiple nodes of a supercomputer, making it possible to rapidly screen molecule spaces with billions of candidates.