As with all newly-introduced methodologies, TORO is not without its limitations. While it shows promise in terms of speed and flexibility, rigorous benchmarking under diverse datasets, experimental conditions, and integration scenarios is still warranted. Initial implementations within CrystFEL have revealed performance bottlenecks that need addressing. Another current limitation of TORO is that it requires a good guess of the initial cell. While this is present in most SX experiments, further development in this direction is needed to cover a wider range of users who might lack this information. Furthermore, to achieve high indexing speed, we need to use batching, which might increase the complexity of a real-time pipeline.