More recently, molecular docking has been incorporated into RL generative model paradigms, offering a proposed solution that integrates structural information, steering molecular design by rewarding compounds that exhibit good docking scores and circumventing some limitations of QSAR models. However, it is often challenging to ascertain what exactly constitutes a 'good' docking score. Docking algorithms are inherently sensitive to the three-dimensional (3D) representation of the protein and ligands. Moreover, different docking configurations may be better suited for particular targets, and performance is intricately dependent on ligand embeddings to sample sufficiently many conformations to access a binding pose deemed favourable. Consequently, while docking scores are a proxy for binding free energies, an accurate prediction is beyond the capability of docking algorithms, caused to a large extent by a lack of sampling of the receptor dynamics and insufficient treatment of entropic effects. Thus, in RL scenarios where docking is a component of the reward function, a poor choice of the docking algorithm or the docking score reward can accentuate the limitations of docking and misinform the agent. Herein, we introduce DockStream, a molecular docking wrapper providing a unified interface to access a collection of ligand embedders and docking backends. DockStream provides an automated and streamlined platform to run docking experiments, supporting a large variety of possible configurations. Automated analysis of docking results expedites search for a docking configuration that performs best for the specific target and set of ligands -i.e. one that displays good correlation with experimental binding affinity or potency. The use of DockStream as a stand-alone tool for SBDD is demonstrated by benchmarking the DEKOIS 2.0 dataset which curates 81 targets with provided sets of active and decoy ligands. The flexibility of DockStream to specify different docking configurations facilitates tailored protocols for different end applications as docking performance necessarily varies depending on the target system. Subsequently, DockStream is integrated with REINVENT 2.0, the recently published de novo design platform. Docking provides structural information to the REINVENT agent, facilitating docking score optimization via RL and steering exploration to relevant chemical space. Using DockStream as a scoring function component enables REINVENT to design compounds that retain key interactions in the binding cavity while simultaneously discovering new ones.