This study shows that a large amount of chemical space can be sampled with generative models that are trained only with a very small sample of that chemical space. Specifically, we generate 68.9% of GDB-13 by only training with 0.1%. The model is not only capable of learning basic chemistry (e.g. valency, ring structures) but also to follow complex constraints applied during the GDB-13 enumeration process, such as heteroatom ratios and positioning of double and triple bonds. More difficult constraints, e.g. complex graph topologies or not allowed functional groups are more difficult to learn mostly due to the limitations of the SMILES notation. We developed a computationally cheap method to monitor and assess the quality of the training process using LL plots. Additionally, we performed sampling of the model and compared the results with those from the ideal model. We obtained that although most of the problematic molecules have a tendency of having more cyclic bonds and heteroatoms, the main difference arises from the SMILES.