A more difficult problem to address is how to prune down the very many generated compounds to the few worth making. In reinforcement learning, a scoring function is used for this purpose. A complicating factor here is that drug discovery is complex and not all factors used in decision-making are easy to capture and thus not readily converted into rules that the AI methods can use in their rewards system. For example, a compound with several stereocenters is usually difficult to make (and resolve) and should consequently get a low reward score unless its building blocks are already available on the shelf. Also, absorption is a critical parameter for the optimization of oral drugs. Permeability over Caco-2 cells is often used as a surrogate when assessing absorption. A complicating factor here is that the uptake over the Caco-2 cells can be hampered by efflux, and in the case of high cell permeation the efflux is less relevant. A reward function handling such scenarios would require several "if-then-else" statements. These can be included in reward scores but are not always trivial to define and set-up for edge cases. In addition, multiparameter optimization becomes increasingly challenging when there are many constraints to fulfill. In brief, the biggest challenge of deep generative methods is to define relevant reward scores, and this is unfortunately less studied.