We have evaluated the effect of RL on the transformerbased molecular generative model trained for generating similar molecules to a given input molecule. The generative model serves as a pre-trained model with knowledge of the chemical space surrounding the input molecule, and reinforcement learning acts as fine tuning phase to focus the model on the desirable chem-ical space based on a set of user-specified property objectives. This provides the flexibility of optimizing molecules towards task-specific property profiles. The evaluation has been performed on two application scenarios, scaffold discovery and molecular optimization. Additionally, the effect of pre-trained priors, learning steps, learning rates and the balancing factor σ was examined. The results have shown that (i) RL generally helps generate more molecules with desired properties compared to No RL for both scaffold discovery and molecular optimization tasks. Additionally, different behaviors can be expected depending on the starting input molecule's structure and properties, e.g. it can be challenging for RL to find molecules with improved activity if the starting molecule is already highly active. (ii) RL consistently helps generating more compounds with desirable properties across priors trained on both ChEMBL and PubChem datasets, and the PubChem prior generally outperforms the ChEMBL prior. (iii) Increasing the number of learning steps typically results in the discovery of more compounds of interest. (iv) Increasing the learning rate (to a certain extent) tends to explore a larger chemical space and sample the chemical space of interest more efficiently, at the same time a higher learning rate leads to a higher variance between different runs. A too high learning rate can have a dramatic negative impact on the performance. (v) For the molecular optimization task, a lower σ typically results in more unique similar molecules, whereas a higher σ tends to produce more unique compounds with improved properties. Ultimately, there is no clear trend in the number of unique compounds that are both similar and show improved properties when varying σ.