To this end, the extensive capabilities of DockStream ligand enumeration provides excellent compatibility with the aforementioned techniques which often require numerous conformational representations. The integration of DockStream with the de novo design platform, REINVENT, enhances its generative capabilities beyond standard QSAR models which often suffer from limited domain applicability. In particular, one is often interested in focusing on either agent chemical space exploration or exploitation, which are both supported in REINVENT. While QSAR models can perform well in the latter scenario provided the predictive model was trained on similar compounds (often belonging to the same series), extrapolating to new chemical space will likely be inaccurate. Molecular docking is chemical space agnostic and provides a more generalized solution to incorporating structural information during the generative process. However, docking will greatly increase the computational costs relative to trained QSAR models, warranting consideration over the quality of compounds generated. To assess the performance of REINVENT-DockStream, 15 different targets from the DEKOIS 2.0 dataset along with their corresponding docking configurations were selected. We emphasize the choice of docking configurations that display enrichment and offer distinction between 'good' docking scores, providing an unambiguous endpoint for docking score optimization and increases the likelihood of generated compounds being true actives. We show that REINVENT-DockStream optimizes docking scores across all docking backends while maintaining "drug-likeliness" as enforced by the QED score. Furthermore, molecular weight was incorporated in the scoring function which helped circumvent artificially high docking scores displayed in relatively large molecules, owing to the sheer number of interactions possible. Large molecules may also be more prone to entropically driven binding, causing off-target effects. Thus, generating compounds that conform to a reasonable size increases the overall quality of the results. Finally, the use of a diversity filter maintains agent exploration as demonstrated by the number of unique SMILES proposed. In order to elucidate the balance between agent exploration and exploitation in REINVENT-DockStream experiments, the average linkage similarity between epochs (Tanimoto) was calculated. The results demonstrate increased intra-batch similarity relative to surrounding epochs, indicating agent sampling from similar chemical space within a given epoch. Moreover, as agent training proceeds, there is a clear transition to greater similarity values at later epochs, paralleling the transition from agent exploration to exploitation. To further assess generated compounds from agent exploitation, the generated top compounds (based on total score) were compared to known actives and known decoys as provided in the DEKOIS 2.0 dataset. While the known actives were not recovered, the top compounds show a clear enrichment in the Tanimoto similarities over the known decoys. The results demonstrate the agent is steered to chemical space similar to that of experimentally validated active compounds. Furthermore, the quality of generated compounds was analyzed by comparing their binding poses relative to known binders. We show that generated compounds retain vital interactions in the binding cavity and exploit new ones. Crucially, the binding poses overlap with known binders, increasing confidence in the prediction. Moreover, we show that 3D structural information is implicitly incorporated during the generative process by exploiting structural motifs and moieties that conform to subtle changes in the binding cavity. Finally, the stochasticity of REINVENT-DockStream was investigated by visualizing the chemical space spanned by the top generated molecules using Uniform Manifold Approximation and Projection (UMAP) as a dimensionality reduction technique. Compared to random sampling, agent generated compounds form clusters over a diverse area and generally converge over replicate runs. Further work will focus on expanding the capabilities of DockStream to address limitations in the reliability of docking scores. Currently, the output binding poses can only be triaged manually but provide an invaluable database for further structure-based methods. In particular, negative image-based rescoring (R-NiB) has been shown to enhance enrichment in terms of activity. Other possible enhancements include an explicit handling of ligand internal strain energy which is typically not well captured by docking algorithms. It has been shown that proper consideration can enhance docking enrichment and limit the number of false positives.