As previously discussed, diversity analysis of chemical space can be used to evaluate and compare different generative approaches. For instance, Arús-Pous et al. used PCA plots of molecular quantum number (MQN) fingerprints to assess the quality of the training process in generative models . In that study, MQN PCA plots allowed the following up and improvement of the comprehension of the varying architectures of molecular generative models. Another recent and representative example of the use of chemical space to analyze diversity was performed with more than 400,000 purchasable building blocks (PBBs) provided by eMolecules ). Visualization of the chemical space of these PBBs using GTM allowed the identification of the most represented and underrepresented classes of PBBs. The results can be focused to improve PBB libraries in a way that allows efficient synthesis in a relevant medicinal chemistry space.