The pharmaceutical industry is investing in continuous flow and high-throughput experimentation as tools for rapid process development accelerated scale-up. Coupled with automation, these technologies offer the potential for comprehensive reaction characterization and optimization, but with the cost of conducting exhaustive multifactor screens. Automated feedback in flow offers researchers an alternative strategy for efficient characterization of reactions based on the use of continuous technology to control chemical reaction conditions and optimize in lieu of screening. Optimization with feedback allows experiments to be conducted where the most information can be gained from the chemistry, enabling product yields to be maximized and kinetic models to be generated while the total number of experiments is minimized. This Account opens by reviewing select examples of feedback optimization in flow and applications to chemical research. Systems in the literature are classified into (i) deterministic "black box" optimization systems that do not model the reaction system and are therefore limited in the utility of results for scale-up, (ii) deterministic model-based optimization systems from which reaction kinetics and/or mechanisms can be automatically evaluated, and (iii) stochastic systems. Though diverse in application, flow feedback systems have predominantly focused upon the optimization of continuous variables, i.e., variables such as time, temperature, and concentration that can be ramped from one experiment to the next. Unfortunately, this implies that the screening of discrete variables such as catalyst, ligand, or solvent generally does not factor into automated flow optimization, resulting in incomplete process knowledge. Herein, we present a system and strategy developed for optimizing discrete and continuous variables of a chemical reaction simultaneously. The approach couples automated feedback with high-throughput reaction screening in droplet flow microfluidics. This Account details the system configuration for on-demand creation of sub-20 μL droplets with interchangeable reagents and catalysts. These droplets are reacted in a fully automated microfluidic system and analyzed online by LC/MS. Feeding back from the online analytical results, a design of experiments (DoE)-based adaptive response surface algorithm is employed that deductively removes candidate reagents from the optimization as optimal reaction conditions are refined, leading to rapid convergence. Using the automated optimization platform, case studies are presented for solvent selection in a competitive alkylation chemistry and for catalyst-ligand selection in heteroaromatic Suzuki-Miyaura cross-coupling chemistries. For the monoalkylation of trans-1,2-diaminocyclohexane, polar aprotic solvents at moderate temperatures are shown to be favorable, with optimality accurately identified with dimethyl sulfoxide as the solvent in 67 experiments. For Suzuki-Miyaura cross-couplings, the optimality of precatalysts and continuous variable conditions are observed to change in accordance with the coupling reagents, providing insights into catalyst behavior in the context of the reaction mechanism. Future opportunities in automated reaction development include the incorporation of chemoinformatics for faster analysis and machine-learning algorithms to guide and optimize the synthesis. Adoption of this technology stands to reduce graduate student and postdoc time on routine tasks in the laboratory, while feeding back knowledge used to guide new research directions. Moreover, the application of this technology in industry promises to lessen the cost and time associated with advancing pharmaceutical molecules through development and scale-up.