Data-driven molecular design is an increasingly pursued strategy in chemical physics and computational chemistry. The search for novel molecules with tailored physicochemical properties for a given functionality is continuously motivating the development of a great variety of computer-aided molecular design approaches . The ultimate goal is to establish a feasible protocol that can be used for exploring the chemical compound space (CCS) through systematic targeting of physical properties. Physicochemical quantities, such as color, conductivity, excited state lifetime, electron affinity, ionization potential, and solubility, are commonly used in the design of molecular photosensitizers or op-toelectronic devices, for instance . Given the complexity of a multi-property design task, it is essential to first have a solid grasp of the physical relationships between the various target properties .