The effectiveness of the optimization approach will be impacted by the properties of the latent space and the performance of the underlying generative auto-encoder model. To evaluate the quality of the latent space and assess whether the autoencoder models have been impacted by posterior collapse we investigated the following: i) reconstruction performance of the auto-encoder models, that is the ability of a model to retrieve a molecule from its latent representation, ii) validity rate, which shows how likely it is for a model to generate a valid SMILES, and iii) continuity of the latent space , which reflects the density of the latent space and how the molecules are clustered in the latent space.