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Flow virometry (FVM) can support advanced water treatment and reuse by delivering near real-time information about viral water quality. But realizing the full potential of FVM in relevant applications relevant to water treatment and reuse requires consistent, optimized protocols to facilitate data validation and interlaboratory comparison—as well as approaches to protocol design that can extend the suite of viruses that FVM can feasibly and efficiently monitor. We address these needs herein. First, we optimize a sample-preparation protocol for a model virus using a fractional factorial experimental design. The final protocol for FVM-based detection of T4—an environmentally relevant viral surrogate—blends and improves on existing protocols developed using a traditional “pipeline”-style optimization approach. Second, we test whether density-based clustering can aid and improve analysis of viral surrogates in complex matrices relative to manual gating. We compare manual gating with results obtained through algorithmic clustering: specifically, by coupling the OPTICS (Ordering Points to Identify Cluster Structure) ordering algorithm with either manual or automated extraction of clusters from the OPTICS-ordered data. We demonstrate that OPTICS-assisted clustering can in some cases work as well or better than manual gating of FVM data—and is far faster and less labor-intensive. OPTICS-assisted clustering can also point to features in FVM data that are difficult to detect through manual gating alone. We demonstrate our combined sample-preparation and automated data analysis pipeline on tertiary-treated wastewater samples collected from a …