Identifying overlaps between error-prone long reads, specifically those from Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PB), is essential for certain downstream applications, including error correction and de novo assembly. Though akin to the read-to-reference alignment problem, read-to-read overlap detection is a distinct problem that can benefit from specialized algorithms that perform efficiently and robustly on high error rate long reads. Here, we review the current state-of-the-art read-to-read overlap tools for error-prone long reads, including BLASR, DALIGNER, MHAP, GraphMap and Minimap. These specialized bioinformatics tools differ not just in their algorithmic designs and methodology, but also in their robustness of performance on a variety of datasets, time and memory efficiency and scalability. We highlight the algorithmic features of these tools, as well as their potential issues and biases when utilizing any particular method. To supplement our review of the algorithms, we benchmarked these tools, tracking their resource needs and computational performance, and assessed the specificity and precision of each. In the versions of the tools tested, we observed that Minimap is the most computationally efficient, specific and sensitive method on the ONT datasets tested, whereas GraphMap and DALIGNER are the most specific and sensitive methods on the tested PB datasets. The concepts surveyed may apply to future sequencing technologies, as scalability is becoming more relevant with increased sequencing throughput.