In this work, similarity search was used in a ranking setup, where the goal is to find the most similar structures in a data set for a given molecular query. First, the molecules from the ChEMBL data set were encoded in their vector representation using algorithmic fragment-based and deep learning approaches. It is important to note, that the ordering of atoms was randomised before encoding of molecular structure in a vector representation. Then a group of 33 molecules were taken from the ChEMBL v.30 data set 13 , which were registered in the US Accepted Names (USAN) in 2021, so these molecules were not present in the ChEMBL v.27 standardised data set. The query molecules were also converted to vector representations with the same parameters as for ChEMBL structures. The 500 most similar structures from the ChEMBL database were identified for each query using the studied vectors. These sets of molecules were then used to compare each approach pairwisely and by visual analysis. The choice of similarity metrics was based on the nature of descriptors. Thus, for fragment-based approaches, Tanimoto coefficient was used, and for continuous vectors, Euclidian distance was employed .