This disagreement highlighted the importance of using native analyte in realistic sample types when screening antibody pairs for assay development. We were unable to completely determine in the course of this work whether performance against GM-rNP or AB-rNP better predicted performance against the diluted clinical sample pool (although neither appeared to predict the latter results as noted above) because every round consisted of a slightly different set of antibodies (as antibodies became commercially available or became depleted from vendor stocks). Surrogate samples can provide the advantages of more reliable sourcing, more experimental control, and less expense than clinical samples. However, it can be difficult to create surrogate samples that are completely representative of real samples, not least due to patient-to-patient variability in the matrix. Additionally, we sourced clinical samples that were stored in VTM, which would not be the diluent used in a clinical LFA test. Nonetheless, we chose to move to the clinical samples at first availability, and to test the best-to-date antibodies still available against a diluted sample pool, to get as close to real samples as possible. While performance (by S-N or S/N) was not necessarily consistent across sample type, one important outcome of running a large antibody screen in an LFA format was the identification of pairs that nonspecifically bind at the test line. Non-specific binding is a major problem for LFA tests, as they represent false positive results in real-world applications. Screening data from non-LFA formats sometimes does not predict non-specific binding in an LFA format, because of the unique interplay of flow dynamics and chemical kinetics across reagents and materials in an LFA. We have found that screening data from the high-throughput robotic platform does predict non-specific binding in the LFA even when screened with different sample matrices, such as clinical negatives at multiple dilutions (Figure ). Additionally, several rounds of negative sample screening data can often be combinedeven if positive samples are varied across rounds-if the negative samples are consistent across rounds, as was the case here. Combined negative sample data was used to remove pairs from contention when non-specific binding was greater than a chosen threshold (e.g., a nominal specificity target), which was helpful because the number of candidate pairs was large. This method reduced the likelihood that a high positive signal was primarily driven by non-specific binding (a false positive), which would incorrectly suggest that a candidate antibody pair deserved further optimization.