The performance of VQGAE was tested in representation learning and generative problems. For the first concept, two common virtual screening tasks were used, namely similarity ranking and QSAR benchmarking. The VQGAE latent vectors were compared with latent vectors from graph-based (HyFactor 8 ) and SMILES-based (LatentGAN 9 ) autoencoders, and with classical chemical descriptors, such as ISIDA fragment descriptors and ECFP fingerprints . It was shown that the VQGAE latent vectors are both atoms order independent and capture structural features with the same efficiency as fragment-based descriptors. The generation ability of the autoencoder was tested in the context of Inverse QSAR, where VQGAE was successfully used to generate new antagonists of the adenosine A2A receptor.