Blocking the catalytic activity of urease has been shown to have a key role in different diseases as well as in different agricultural applications. A vast array of molecules have been tested against ureases of different species, but the clinical translation of these compounds has been limited due to challenges of potency, chemical and metabolic stability as well as promiscuity against other proteins. The design and development of new compounds greatly benefit from insights from previously tested compounds; however, no large-scale studies surveying the urease inhibitors' chemical space exist that can provide an overview of developed compounds to data. Therefore, given the increasing interest in developing new compounds for this target, we carried out a comprehensive analysis of the activity landscape published so far. To do so, we assembled and curated a data set of compounds tested against urease. To the best of our knowledge, this is the largest data set of urease inhibitors to date, composed of 3200 compounds of diverse structures. We characterized the data set in terms of chemical space coverage, molecular scaffolds, distribution with respect to physicochemical properties, as well as temporal trends of drug development. Through these analyses, we highlighted different substructures and functional groups responsible for distinct activity and inactivity against ureases. Furthermore, activity cliffs were assessed, and the chemical space of urease inhibitors was compared to DrugBank. Finally, we extracted meaningful patterns associated with activity using a decision tree algorithm. Overall, this study provides a critical overview of urease inhibitor research carried out in the last few decades and enabled finding underlying SAR patterns such as under-reported chemical functional groups that contribute to the overall activity. With this work, we propose different rules and practical implications that can guide the design or selection of novel compounds to be screened as well as lead optimization.