Nevertheless, a significant roadblock for adopting methodologies based on generative ML models in practical applications in small molecule drug discovery is synthesizability of generated molecules. As we are moving beyond the known chemical space generating novel structures, synthesizability is not guaranteed. This could be addressed either at the pertaining stage, when the latent space is being formed, or during the optimization phase as part of the reward function. However, the effectiveness of the tools for estimating synthesizability of generated molecules remains inadequate. Optimization efficiency, that is the degree of exploration needed to reach an optimal solution, is an additional challenge for practical applications, especially for accommodating computationally demanding reward functions such as molecular docking.