Figure illustrates the whole domain of possible outcomes from a RNN trained with SMILES. This domain changes during the training process: before training the RNN generates random strings, a few of which are going to be valid SMILES. After training, the generated strings are mostly valid SMILES that, to a large extent, belong to GDB-13. By computing how much of the whole GDB-13 a model can generate from a small subset and which molecules outside of the domain of GDB-13 are generated, the learning limitations are assessed. To do this, the results obtained from the trained model are compared to those from an abstract ideal model which generates all GDB-13 molecules with uniform distribution. Any model, regardless of its architecture or input format, trained with a subset of GDB-13 can be compared to this ideal model in the same manner, thus creating a new way to benchmark the limitations of models prior to using them to explore chemical space.