The conventional Internet is acquiring a geospatial dimension. Web documents are being geo-tagged and geo-referenced objects such as points of interest are being associated with descriptive text documents. The resulting fusion of geo-location and documents enables new kinds of queries that take into account both location proximity and text relevancy. This paper proposes a new indexing framework for top-k spatial text retrieval. The framework leverages the inverted file for text retrieval and the R-tree for spatial proximity querying. Several indexing approaches are explored within this framework. The framework encompasses algorithms that utilize the proposed indexes for computing location-aware as well as region-aware top-k text retrieval queries, thus taking into account both text relevancy and spatial proximity to prune the search space. Results of empirical studies with an implementation of the framework demonstrate that the paper’s proposal is capable of excellent performance.