A significant barrier inhibiting multiplexed imaging in the near-infrared (NIR) is the extensive trial and error associated with fine-tuning NIR dyes. In particular, the need to synthesize and experimentally evaluate dye derivatives in order to empirically identify those that can be used in multiplexing applications, requires a large investment of time. While coarse-tuning efforts benefit from computational prediction that can be used to identify target dye structures for synthetic campaigns, errors in computational prediction remain too large to accurately parse modifications aimed at fine-tuning changes in dye absorbance and emission. To address this issue, we screened different levels of theory and identified a time-dependent density functional theory (TD-DFT) approach that can rapidly, as opposed to synthesis and experimental evaluation, estimate absorbance and emission. By calibrating these computational estimations of absorbance and emission to experimentally determined parameters for a panel of existing NIR dyes, we obtain calibration curves that can be used to accurately predict the effect of fine-tuning modifications in new dyes. We demonstrate the predictive power of this calibrated dataset using seven previously unreported dyes, obtaining mean percent errors in absorbance and emission of 2.2 and 2.8 %, respectively. This approach provides a significant timesavings, relative to synthesis and evaluation of dye derivatives, and can be used to focus synthetic campaigns on the most promising dye structures. The new dyes described herein can be utilized for multiplexed imaging, and the experimentally calibrated dataset will provide the dye chemistry community with a means to rapidly identify fine-tuned NIR dyes in silico to guide subsequent synthetic campaigns.