Locality Sensitive Hashing (LSH) methods are being successfully employed for scaling similarity queries or similarity joins to large databases. We show that operations requiring intensive kernel computations can also benefit from the use of LSH. Specifically, this allows to accelerate active learning methods for which the query corresponds to a class boundary.