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Optimizing empirical baseflow filters using environmental tracers (e.g., specific electrical conductance (SEC), turbidity) is an effective and efficient way to quantify the contribution of baseflow to total flow. To execute this baseflow separation, three key components are needed: The tracer, the method to estimate tracer concentration in different flow components, and the empirical baseflow filter. However, a comprehensive evaluation of the various combinations of these components, especially with a large sample of catchments, is currently lacking in the literature. Therefore, our study assembles 16 hybrid baseflow filters from two tracers, two concentration estimation methods, and four empirical baseflow filters, and evaluated their performance in baseflow separation and producing two long‐term baseflow signatures for 1,100 catchments in the Contiguous United States. Our results suggest that SEC is a superior tracer to turbidity for baseflow separation. Additionally, using monthly maximum and minimum values to represent tracer concentration in flow components produces better separation than using a power function relationship between flow rate and concentration. The four empirical baseflow filters offer a similar level of performance, regardless of the other options used. Yet, some of these filters produce inconsistent results in calculating the baseflow signatures for the catchments. Our analysis shed light on the optimization of hybrid baseflow filters for the accurate quantification of baseflow contribution. Plain Language Summary: River flow can be broken down into two components: fast flow and slow flow. The latter is usually known as baseflow, and it represents the stable portion of river flow that comes from stored water sources, such as groundwater or snowpack. It is crucial to understand the proportion of baseflow in river flow for effective water resource management. A commonly used method to separate baseflow from river flow is by filtering streamflow data with empirical baseflow filters. These filters contain some parameters that are often optimized using geochemical data, such as specific electrical conductance (SEC) and turbidity, to ensure reasonable performance of baseflow separation. This study examined how SEC and turbidity can be used to optimize four empirical baseflow filters for quantitative assessment of baseflow contribution to streamflow. Our analysis of 1,100 catchments across the Contiguous United States revealed that SEC is a more reliable indicator of baseflow than turbidity. Interestingly, the choice of empirical baseflow filter had minimal impact, though some filters produced inconsistent results for the quantification of baseflow contribution. This research enhances our ability to accurately estimate baseflow, aiding in water resource planning and management. Key Points: Evidence suggests that specific electrical conductance is a better tracer for baseflow separation compared to turbidityUsing monthly extreme values to describe tracer signature in flow components is better than using a power function relationshipThe smooth minima method provides the most consistent estimation of baseflow contribution across various combinations [ABSTRACT FROM AUTHOR]