Constructing novel molecules from scratch using deep generative models provides useful alternative to traditional virtual screening methods which are limited to the search of the already discovered chemicals. In particular, molecular optimisation combined with sampling guided by reinforcement learning seems like a promising path for discovering novel molecular designs and allows for domain-specific customization of the desired solutions. The choice of a chemically relevant reward function and the exhaustive assessment of its properties remains a challenging task. We introduce the reward function which gives enough flexibility to quantify the biological activity with respect to a selected protein target, drug-likeness, synthesizability and incorporates the custom index of penalised physico-chemical properties. In order to customise the hyper-parameters influencing the RL agent performance, wepropose the methodology which helps to quantify the chemical relevance of the reward function by quantifying the chemical relevance of the samples. We assess the performance of the reward function by docking the molecules with relevant protein targets and quantify the difference with the ground truth samples using Wasserstein distance.