Research fields that leverage relational data, like many others, have been significantly impacted by Deep Learning (DL) techniques, particularly Graph Neural Networks (GNNs). Among these fields, drug design, which aims to create new molecules with optimal affinities for specific targets, is a crucial step in the development of new medicinal drugs. In silico approaches in this area often rely on molecular graphs that encode the atoms and bonds of a molecule, without prior knowledge of the biological properties to be predicted. To address this limitation, pharmacophoric features are essential, as they contain structural information that captures important biological properties. These features have proven effective in tasks involving protein-ligand interactions. In this context, we propose the MCP-GNN model, which combines molecular representations with complete graphs of pharmacophoric features, both based on 2D information, to classify biological activity. Our experimental results demonstrate that this approach, using simple yet efficient techniques, achieves better performance than more complex architectures.