Due to the overgrowth of GPS-equipped mobile devices (as smartphones, smartwatches, etc.) and IoT environments, location-based services are generating a large number of objects that contain both spatial and textual information, and therefore, spatio-textual databases have received significant attention in recent years. This paper addresses the problem of Spatio-Textual Similarity Join (STSJ), which outputs object pairs that can be important in applications such as social networking, tourism, etc. This join operation consists of, given a set of objects that contain both spatial and textual information, returning pairs of objects that are spatially close and textually similar. To accomplish this, we design and implement four different algorithms to evaluate the STSJ query using the IR-tree in main memory. Two of them are iterative algorithms (Best-First traversal) and the other two are recursive versions (Depth-First approach). For both iterative and recursive algorithms, we use the plane-sweep technique and the smart all-by-all algorithm to improve query processing. Finally, we conducted extensive experiments on real-world datasets to evaluate the performance of our algorithms for STSJ. The main performance conclusion is that the Best-First algorithm with the smart all-by-all combination has excellent performance for small-medium dataset sizes, while the recursive approach with the same combination reports the best performance for larger ones.