As a consequence, several derivatives from registered AMPs such as LL-37 and Indolicidin, from human and bovine origin, respectively, have been synthetized based on the potent antibiofilm/ antibacterial actions of their natural templates [20]. This background points out AMPs and their synthetic relatives as promising alternatives for developing antibiofilm agents, also leveraging the multi-functionality of the AMPs class through their immunomodulatory, anti-inflammatory, wound-healing, anti-fungal and antibacterial activities, especially their potentialities to target MDR planktonic strains [21; 22]. It is also important to highlight the relative low cost of peptides' production in comparison with other emerging therapeutic alternatives [23]. Thus, their antibiofilm activity has gained attention in the AMPs world, being evaluated in vitro and in vivo models and registered either in general AMP databases such as UniProtKB [24], the Antimicrobial Peptide Database (APD) [25], the Data Repository of Antimicrobial Peptides (DRAMP) [26], or in others exclusively dedicated to antibiofilm peptides, such as the Biofilm-active AMPs (BaAMPS) database [27]. This last specific database has been useful for researchers to train machine learning (ML) models to predict antibiofilm activity [28; 29; 30; 31]. Despite efforts to populate AMPs databases and build ML models to assist the discovery/design of antibiofilm peptide drugs, the success rate has been extremely low, considering there is no antibiofilm agent in clinical use [5]. Therefore, the development of new in silico approaches, providing new insights into the classical drug discovery pipeline, is always welcome. Thus, emerging tools and databases based on graph and networks science could be integrated in the discovery/design pipeline of peptide drugs through analyzing the chemical space of bioactive peptides [32; 33; 34]. Consequently, the relevant features of antibiofilm peptides (ABFPs) can be approached by applying complex networks to identify privileged scaffolds and motifs, aiming to develop next-generation antimicrobials able to combat MDR microbes by either targeting their biofilm or planktonic forms. Namely, complex networks can be useful for (i) analyzing the structural diversity of ABFPs according to the network topology/modularity, (ii) selecting the most representative and atypical ABFPs, determined mainly by centrality measures, (iii) identifying relevant functionalities (other than the antibiofilm contributing to efficient antimicrobial activity) by weighting their associated metadata with centrality measures, (iv) identifying ABFP motifs within networks communities, and (v) determining of a reduced set of peptides that actually represents the original antibiofilm chemical space. Here, the half-space proximal network (HSPN) was applied to represent the structural (sequence-based) space of anti-biofilm peptides registered in the graph-based database (StarPepDB) [35], which probably has the most comprehensive subset of antibiofilm peptides collected from both generic AMPs databases and specific databases for antibiofilms (Available online: [[http://mobiosd-hub.com/starpep/](http://mobiosd-hub.com/starpep/)]([http://mobiosd-hub.com/starpep/](http://mobiosd-hub.com/starpep/)) (accessed on 5 January 2023)). The HSPN is a local proximity graph that extracts a low-degree spanner of the complete graph. Each node is associated with its nearest neighbor, clearing from the complete graph all redundant nodes in the nearest neighbor's direction by using a half-space hyperplane [36; 37]. HSPNs do not consider all the possible pairwise relationships between nodes; instead, these networks apply the half-space proximal test over the set of nodes, obtaining a connected network with a smaller fraction of the maximum number of edges. As they show a lower density, a lesser RAM memory is needed for their construction; therefore, they were recently applied by our group to study the chemical space of antiparasitic peptides [34].