The One-Shot Similarity measure has recently been introduced as a means of boosting the performance of face recognition systems. Given two vectors, their One-Shot Similarity score reflects the likelihood of each vector belonging to the same class as the other vector and not in a class defined by a fixed set of "negative™ examples. An appealing aspect of this approach is that it does not require class labeled training data. In this paper we explore how the One-Shot Similarity may nevertheless benefit from the availability of such labels. We make the following contributions: (a) we present a system utilizing subject and pose information to improve facial image pair-matching performance using multiple One-Shot scores; (b) we show how separating pose and identity may lead to better face recognition rates in unconstrained, "wild™ facial images; (c) we explore how far we can get using a single descriptor with different similarity tests as opposed to the popular multiple descriptor approaches; and (d) we demonstrate the benefit of learned metrics for improved One-Shot performance. We test the performance of our system on the challenging Labeled Faces in the Wild unrestricted benchmark and present results that exceed by a large margin results reported on the restricted benchmark.