Activity cliffs are small changes in the structure associated with unexpected large changes in biological activity . Identification of activity cliffs is a relevant and informative analysis of the SAR because they point to specific and minor structural modifications that strongly influence biological activity. One must be aware, however, that there might be "artificial" cliffs in compound data sets, for instance, the biological activity is not measured correctly . In this work, to identify the possible presence of activity cliffs in the DS, we calculated consensus activity cliffs between multiple 2D fingerprints (MACCS Keys 166-bits, ECFP4, ECFP3, ECFP2, and path length), founding the activity cliff common in all fingerprints through of Tanimoto coefficient as is proposed by Medina-Franco and coworkers . RDKit and MayaChemTools were employed for this analysis.