MAD (Mapping to Activity class-specific Descriptor value ranges) is a novel molecular similarity method that relies on the identification of activity-specific descriptors. Applying a categorical descriptor scoring function, value ranges of molecular descriptors in screening databases are compared with those in classes of active compounds and descriptors displaying significant deviations are selected. In order to identify new actives, database molecules are mapped to class-specific value ranges and ranked using a similarity function. As a mapping algorithm, MAD is distinct from many other molecular similarity and virtual screening methods. In systematic virtual screening trials, for small selection sets of only 30 database compounds, average hit and recovery rates over six activity classes ranged from about 10% to 25% and about 25% to 75%, respectively. Moreover, when mining a database of bioactive molecules many similar compounds were selected (with hit rates between about 15% and 79%). Our findings suggest that it is possible to generate compound class-directed descriptor reference spaces for molecular similarity analysis.