Mobile search is quickly becoming the most common mode of search on the internet. This shift is driving changes in user behaviour, and search engine behaviour. Just over half of all search queries from mobile devices have local intent, making location-aware search an increasingly important problem. In this work, we compare the efficiency and effectiveness of two general types of geographical search queries, range queries and $k$-nearest-neighbor queries, for common web search tasks. We test state-of-the-art spatial-textual indexing and search algorithms for both query types on two large datasets. Finally, we present a rank-safe dynamic pruning algorithm that is simple to implement and use with current inverted indexing techniques. Our algorithm is more efficient than the tightly coupled best-in-breed hybrid indexing algorithms that are commonly used for top-$k$ spatial textual queries, and more likely to find relevant documents than techniques derived from range queries.