To date, enormous studies have been devoted to investigate biochemical functions of receptors, which have crucial roles for signal processing in organisms, and ligands are key tools in experiments since receptor specificity with respect to them enables us to control activity of receptors. However, finding ligands is difficult; choosing ligand candidates relies on expert knowledge of biologists and conducting test experiments in vivo or in vitro costs high. Here we challenge to ligand finding with a machine learning approach by formalizing the problem as multi-label classification mainly discussed in the area of preference learning. We develop in this paper a new algorithm LIFT (Ligand FInding via Formal ConcepT Analysis) for multi-label classification, which can treat ligand data in databases in the semi-supervised manner. The key to LIFT is to realize clustering by putting an original dataset on lattices using the data analysis technique of Formal Concept Analysis (FCA), followed by obtaining the preference for each label using the lattice structure. Experiments using real data of ligands and receptors in the IUPHAR database show that LIFT effectively solves our task compared to other machine learning algorithms.To date, enormous studies have been devoted to investigate biochemical functions of receptors, which have crucial roles for signal processing in organisms, and ligands are key tools in experiments since receptor specificity with respect to them enables us to control activity of receptors. However, finding ligands is difficult; choosing ligand candidates relies on expert knowledge of biologists and conducting test experiments in vivo or in vitro costs high. Here we challenge to ligand finding with a machine learning approach by formalizing the problem as multi-label classification mainly discussed in the area of preference learning. We develop in this paper a new algorithm LIFT (Ligand FInding via Formal ConcepT Analysis) for multi-label classification, which can treat ligand data in databases in the semi-supervised manner. The key to LIFT is to realize clustering by putting an original dataset on lattices using the data analysis technique of Formal Concept Analysis (FCA), followed by obtaining the preference for each label using the lattice structure. Experiments using real data of ligands and receptors in the IUPHAR database show that LIFT effectively solves our task compared to other machine learning algorithms.