SimRank is an intuitive and effective measure for link-based similarity that scores similarity between two nodes as the first-meeting probability of two random surfers, based on the random surfer model. However, when a user queries the similarity of a given node-pair based on SimRank, the existing approaches need to compute the similarities of other node-pairs beforehand, which we call an all-pair style. In this paper, we propose a Single-Pair SimRank approach. Without accuracy loss, this approach performs an iterative computation to obtain the similarity of a single node-pair. The time cost of our Single-Pair SimRank is always less than All-Pair SimRank and obviously efficient when we only need to assess similarity of one or a few node-pairs. We confirm the accuracy and efficiency of our approach in extensive experimental studies over synthetic and real datasets.