The assumption of positive-definiteness is restrictive, as it limits the number of applicable functions. Recent developments in the theory of learning with indefinite kernels have now removed this requirement, allowing a much broader class of functions to be incorporated into kernel methods . Leveraging these developments, we study the effectiveness of learning with established sequence-similarity functions for the classification of antimicrobial peptides (AMPs) based on their amino acid sequences. We evaluate the ability of the proposed methodology to predict both general and species-specific antimicrobial activity.