Numerous empirical algorithms have been operationally used to retrieve global ocean chlorophyll-a (Chla) concentrations from ocean color satellite data, for example, the OC4V4 algorithm for sea-viewing wide field-of-view sensor and the OC3M algorithm for moderate-resolution imaging spectroradiometer. However, the algorithms have been established and validated based on in situ data mainly measured under low to moderate solar zenith angle (SZA) (<70 deg). Currently, with the development of geostationary satellite ocean color remote sensing, enabling observations from early morning to late afternoon, it has become necessary to know whether the empirical Chla algorithms can be applied to high SZA. The performances of seven widely used Chla algorithms (i.e., OC2, OC3M, OC3V, OC4V4, Clark, ocean-color index, and Yellow Sea Large Marine Ecosystem Ocean Color Work Group) under high SZAs were evaluated using the global in situ ocean color dataset (NASA bio-optical marine algorithm dataset). The results show that the performances of all seven algorithms decreased significantly under high SZAs compared with those under low to moderate SZAs. For instance, for the OC4V4 algorithm, the relative percent difference (RPD) and root-mean-square error (RMSE) were 13.78% and 1.66 μg/L for the whole dataset and 3.95% and 1.49 μg/L for SZAs ranging from 30 deg to 40 deg, respectively. However, RPD and RMSE values increased to 30.45% and 6.10 μg/L for SZAs larger than 70 deg. Adjusting the coefficients of the algorithms using the in situ dataset with high SZA can only slightly improve the performance. The bidirectional remote sensing reflectance can explain the underestimation of the retrieved Chla under high SZA, and the low sensitivity of the blue–green ratio is responsible for the relatively larger scatter between the retrieved and the in situ Chla under high SZA.