Figure shows a clear difference in the arrangement of the dataset is observed when modifying the visualization technique and the molecular representation, resulting in distinct local clusters within the chemical space shared. Analysis of the visual representation of the chemical space is based on the similarity principle, compounds with similar properties (e.g., biological activity) share similar structural characteristics. Consequently, the formation of several local clusters, combined with the activity observed for each molecular target, enable a deeper level of analysis. This analysis suggests that a higher density of points in local clusters with a lower activity index correlates with a greater contribution of that property to the reported experimental activity. However, in regions where there is a mixture of data points with multiple sizes, it is observed that the representation used is not efficient in capturing the characteristics associated with the biological activity. This type of data exploration analysis that can be performed with MAYA is not limited to inhibition activity criteria. The user can analyze basically any type of biological activity data, properties or calculated scores, allowing for a broader evaluation of the chemical space.