Once the autoencoder has been trained, it is important to understand the quality of the latent vectors and its generative ability. These characteristics are non-trivial to measure, as there are no existing metrics for them. However, one way to analyse the performance of the autoencoder's latent space is through the similarity ranking task. The similarity search provides insight into the neighbourhood behaviour of the latent space, which is the basis for any modelling using the vector representation of molecules.