The emerging concept of the activity landscape has been widely applied for structureactivity relationships (SAR) characterization. Since chemical space representation plays a crucial role in activity landscape modeling, an adequate selection of similarity measures is desirable. Herein a set of 658 cyclooxygenase inhibitors were structurally analyzed using 12 molecular similarity representations and two levels of chemotype classification. Then, three uncorrelated similarity measures and mean similarity (obtained with data fusion) were combined with chemotype information using the herein proposed chemotypesimilarity graphs. Chemotype-similarity graphs showed the cumulative distribution of molecular pairs with the same and different chemotype along similarity values; leading to establish an interpretable, quantitative and activity independent threshold in similarity measures based on chemotype distributions. This approach gave additional information to similarity measures and can be considered as an interpretable criterion to define high and low similar compounds. The results were applied to model the activity landscape using StructureActivity Similarity (SAS)-like maps. Some examples of pairs in each region of the SAS-like maps were analyzed which showed valuable SAR information for cyclooxygenase inhibitors.