Fig. illustrates schematically a pipeline for the adaptive training of the GCNN surrogate model and the generation of new molecules with desirable HOMO-LUMO gap as predicted by the DFTB method. We begin by performing DFTB calculations on a subset of the 134,000 molecules contained in the QM9 database to predict their HOMO-LUMO gaps, then train the GCNN surrogate which is subsequently utilized to score the newly generated molecules predicted by the MLM algorithm. In a final step, the newly predicted molecules are re-calculated using the DFTB method and their HOMO-LUMO gaps compared against the surrogate-predicted value (not shown in Fig. ). In the following, we briefly discuss the center pieces of our workflow, namely the DFTB calculations of HOMO-LUMO gaps from SMILES strings, the GCNN surrogate and the MLM, and their interplay with each other, before elaborating the details of each component in subsequent Sections. DFTB To generate on-the-fly HOMO-LUMO gap predictions, we employ the density-functional tight-binding (DFTB) method. DFTB is a fast and efficient quantum mechanical simulation technique that is implemented e.g. in the DFTB+ program package , as well as in other popular quantum chemistry packages. The required runs are performed in two stages. In the first stage we generate the data for predicted HOMO-LUMO gaps from a Simplified Molecular Input Line Entry System (SMILES) string representing the molecular structure. If the structures are in PDB format they can be easily transformed into SMILES representation using the RDKit software . All of the results are merged into a single file and curated for the GCNN surrogate operation.