Abstract: We present low-dimensional latent representations learnt by the $\beta-$VAEs from the graph-topological structures encoding pharmacophoric features. The controlled information compression of these molecular fingerprints effectively removes the ambiguous redundancies and consequently results in encoding the chemically semantic latents. This latent molecular semantics allows for various tasks, from molecular similarity assessment to better-targeted search of the chemical space and drug discovery. We investigate the performance of the learnt latents of various dimensions on the ligand-based virtual screening task.