Current methodologies that have been used to study P-SPR have limitations, and the activity landscape approximation presented in this work is not an exception. The fingerprint-based similarity (using MAP4 and the MinHassed distance) is a new method to explore and describe the landscape of any property in peptides. However, the results of this study suggest that this methodology could be highsensitive to structural changes on peptides with less than 20 AA, which could limit their applicability, and remarks on the importance to develop new molecular representations focused on peptides. For this reason, we recommend using multiple criteria and methodologies to understand the P-SPR: activity land. complementing this method using classical alignment sequence analysis, and 3D approximation to help understand the P-SPR. In this regard, the present work contributes to establishing a helpful workflow based on structure similarity metrics to explore P-SPRs and quickly identify peptide activity cliffs.