In this work we present a vision for a DFTB-and TD-DFTB-based computational workflow for the inverse design of molecules with desirable optical properties. As others before us, we start with an approximation of excitation energies by MO energy differences . Besides the use of computationally efficient DFTB methods, our new inverse design algorithm has two major components: 1) Development of a multi-headed graph convolutional neural network (GCNN) approach that will allow the prediction of not only molecular orbital energy differences, but more complex properties such as multiple excitation energies and oscillator strengths for dye candidates . The GCNNs surrogate models are sufficiently fast to supplant computationally expensive, explicit excited state calculations required for the inverse design step, which will be component 2): Development of a machine learning masked language model (MLM)-based generation of new dye candidates with subsequent evaluation against a target function (desirable optical properties). As proof-of-principle, we present here a study for the inverse design of molecules with the lowest-possible HOMO-LUMO gap, which is motivated by possible applications as biomarkers in biomedical applications . We note that our choice to minimize the HOMO-LUMO gap in this proposed workflow is arbitrary and could be replaced by a particular energy range. Our choice of a multi-headed GCNN surrogate will allow in subsequent works to extend our molecular design algorithm to predict novel molecules with optical spectra containing user-defined target features, such as intense optical absorption or emission in particular regions of the visible light spectrum.