Similarity searching is an important problem for complex and unstructured data such as images, video, and text documents. One common solution is approximating complex objects into feature vectors. Metric spaces approach, on the other hand, relies solely on a distance function between objects. No information is assumed about the internal structure of the objects, therefore a more general framework is provided. Methods that use the metric spaces have also been shown to perform better especially on high dimensional data. A common query type used in similarity searching is the range query, where all the neighbors in a certain area defined by a query object and a radius are retrieved. Another important type, k-nearest neighbor queries return k closest objects to a given query center. They are more difficult to process since the distance of the kth nearest neighbor varies highly. For that reason, some techniques are proposed to estimate a radius that will return exactly k objects, reducing the computation into a range query. A major problem with these methods is that multiple passes over the index data is required if the estimation is low. In this thesis we propose a new framework for k-nearest neighbor search based on radius estimation where only one sequential pass over the index data is required. We accomplish this by caching a short-list of promising candidates. We also propose several algorithms to estimate the query radius which outperform previously proposed methods. We show that our estimations are accurate enough to keep the size of the promising objects at acceptable levels.