The distance similarity search (DSS) is a fundamental operation for large-scale data analytics, as it is used to find all points that are within a search distance of a query point. Given that new scientific instruments are generating a tremendous amount of data, it is critical that these searches are highly efficient. Recently, GPU algorithms have been proposed to parallelize the DSS. While most work shows that GPU algorithms largely outperform parallel CPU algorithms, there is no single GPU algorithm that outperforms all other state-of-the-art approaches; therefore, it is not clear which algorithm should be selected based on a dataset/workload. We compare two GPU DSS algorithms: one that indexes directly on the data coordinates, and one that indexes using the distances between data points to a set of reference points. A counterintuitive finding is that the data dimensionality is not a good indicator of which algorithm should be used on a given dataset. We also find that the intrinsic dimensionality (ID) which quantifies structure in the data can be used to parameter tune the algorithms to improve performance over the baselines reported in prior work. Lastly, we find that combining the data dimensionality and ID can be used to select between the best performing GPU algorithm on a dataset.