We present our progress on overcoming key challenges in applying generative models to 3D ligand design, including generating high-quality binders and reducing inference times. We introduce FLOWR, a flow matching framework for 3D ligand generation conditioned on a protein pocket and a set of desired interaction between the protein and the ligand. To thoroughly evaluate our model we also introduce SPIRE, a refined dataset of high-quality protein-ligand complexes derived from crystallographic data. Evaluations on this dataset show that FLOWR outperforms an existing state-of-the-art diffusion model, while achieving up to a 50-fold speed-up in inference time. We also propose an interaction-aware training and inference strategy that enables the generation of novel ligands tailored to predefined interaction profiles. Our findings suggest that FLOWR is an important step forward for efficient, AI-driven de novo ligand generation.