The 'roughness' of the molecular activity landscape can provide insights into the level of difficulty that machine learning models are expected to encounter and also reveal challenging dataset characteristics such as activity cliffs. To quantify this property, we use the recently-introduced roughness index (ROGI) , which can be applied only to the DR datasets as its computation scales quadratically with the dataset size. Intuitively, the ROGI works by coarse-graining a molecular dataset at different levels and quantifying the change in dispersion, which might be affected rapidly or slowly. The notion of dispersion is quantified using the standard deviation of the molecular property. This is related to the idea that similar molecules with extremely different properties (e.g. activity) lead to rapidly changing dispersion.