  Motivated by an application to drug repurposing, we propose the first
algorithms to tackle the identification of the m $\ge$ 1 arms with largest
means in a linear bandit model, in the fixed-confidence setting. These
algorithms belong to the generic family of Gap-Index Focused Algorithms (GIFA)
that we introduce for Top-m identification in linear bandits. We propose a
unified analysis of these algorithms, which shows how the use of features might
decrease the sample complexity. We further validate these algorithms
empirically on simulated data and on a simple drug repurposing task.
