Increasing volumes of geo-referenced data are becoming available. This data includes so-called points of interest that describe businesses, tourist attractions, etc. by means of a geo-location and properties such as a textual description or ratings. We propose and study the efficient implementation of a new kind of query on points of interest that takes into account both the locations and properties of the points of interest. The query takes a result cardinality, a spatial range, and property-related preferences as parameters, and it returns a compact set of points of interest with the given cardinality and in the given range that satisfies the preferences. Specifically, the points of interest in the result set cover so-called allying preferences and are located far from points of interest that possess so-called alienating preferences. A unified result rating function integrates the two kinds of preferences with spatial distance to achieve this functionality. We provide efficient exact algorithms for this kind of query. To enable queries on large datasets, we also provide an approximate algorithm that utilizes a nearest-neighbor property to achieve scalable performance. We develop and apply lower and upper bounds that enable search-space pruning and thus improve performance. Finally, we provide a generalization of the above query and also extend the algorithms to support the generalization. We report on an experimental evaluation of the proposed algorithms using real point of interest data from Google Places for Business that offers insight into the performance of the proposed solutions.