The analysis of the generated molecular scaffolds holds great importance in drug discovery. On one hand, preserving "privileged" molecular scaffolds for bioactivity can serve for molecule optimization, and, on the other hand, the exploration of structurally distinct compounds having similar activity can accelerate the identification of new therapeutic agents with improved efficacy and selectivity. For this reason, we used the results of all transfer learning experiments to analyse the generated molecular scaffolds (computed via the Bemis-Murcko 45 algorithm).