Active learning consists in sequentially and adaptively constructing a data-set in the hope of improving the learning speed by avoiding useless data-points where the current model is already correct with large probability and by focusing on uncertainty regions. During this talk, I will give a short reminder on the potential benefits and pitfalls of active learning, especially in large and combinatorial models.