A new and versatile visualization tool, based on a descriptor accounting for ligand-receptor interactions (LiRIf), is introduced for guiding medicinal chemists in analyzing the R-groups from a congeneric series. Analysis is performed in a reference-independent scenario where the whole biologically relevant chemical space (BRCS) is represented. Using a real project-based data set, we show the impact of this tool on four key navigation strategies for the drug discovery process. First, this navigator analyzes competitors' patents, including a comparison of patents coverage and the identification of the most frequent fragments. Second, the tool analyzes the structure-activity relationship (SAR) leading to the representation of reference-independent activity landscapes that enable the identification not only of critical ligand-receptor interactions (LRI) and substructural features but also of activity cliffs. Third, this navigator enables comparison of libraries, thus selecting commercially available molecules that complement unexplored spaces or areas of interest. Finally, this tool also enables the design of new analogues, which is based on reaction types and the exploration purpose (focused or diverse), selecting the most appropriate reagents.