The "design and perfection of atom-and energy-efficient synthesis of revolutionary new forms of matter with tailored properties" is one of five scientific grand challenges articulated in the Basic Energy Sciences Advisory Report on Directing Matter and Energy . In chemical sciences, one of the most coveted and impactful targets is the ability to design molecular compounds with desirable properties such as biological activity for molecular therapeutics or particular photo-optical properties geared towards photovoltaic applications , or the development of molecular dyes or biomarkers . While significant progress has already been achieved in the field of machine learning-assisted computational drug discovery , the use of artificial intelligence (AI) protocols for the design of photoactive molecules is still in its infancy . This situation can be attributed in part to the fact that the prediction of photo-optical properties for a given molecular structure requires computationally expensive quantum chemical calculations, namely the computation of molecules in their ground and excited states . The computational generation of sufficiently large databases containing molecular structure and their optical properties is therefore far more costly than the calculation of bioactivity, which is traditionally performed using computationally much cheaper empirical scoring or classical force field calculations of protein-ligand interactions .