The current proof-of-principle shows that the combination of semiempirical quantum chemical electronic structure theory, GCNN surrogate, and MLM generative model succeeds to predict a plethora of novel molecular compounds with desirable optical properties, in this case HOMO-LUMO gaps that are as small as possible. First we wish to mention how well our multi-headed GCNN surrogate reproduced HOMO-LUMO gaps, the MAE was 0.11 eV for molecules both in the training as well as test set (both part of the QM9 database). This accuracy is similar to the one recently reported by Lilienfeld et al. and is comparable, or even exceeds, the error bars that can be expected from traditional DFT methods in their prediction of excitation energies, and thus lays the foundation for an inverse design workflow focusing on HOMO-LUMO gaps. The surrogate lost accuracy when trying to predict HOMO-LUMO gaps of larger molecules that were not part of the training set, which indicates the necessity of adaptive surrogate training schemes in iterative molecular design. Our computational workflow for accelerated molecular design using quantum chemical simulations and deep learning models already possesses these capabilities, and we will exploit them in future applications.