Energy-efficient computing is a well-studied and established field. Software energy-efficiency is one of the ways to decrease energy consumption of computing systems. However, contemporary studies on energy-efficient software investigate only individual algorithms, neglecting such an important area as workflow energy-efficiency. In this paper we try to decrease this gap by providing a study which investigates dependencies between software algorithms organized in a workflow. We empirically study the effect of dynamic voltage and frequency scaling and dynamic concurrency throttling on energy consumption of two case studies: workflows combined from (a) compression and encryption algorithms; and (b) matrix transposition and addition. Our findings show, that a suitable structure of a workflow, can significantly reduce energy consumption of the overall system. However, empirical studies of workflow energy-efficiency are themselves very time-and energy-demanding. Therefore, we provide an approach called Star-BRISE that allows to reuse data obtained from benchmarking of individual algorithms to decrease the amount of measurements for resulting workflows. The presented approach can save up to 78% of time and energy effort on finding an optimal configuration for 2-algorithm workflows (and up to 95 % of effort for a single workflow) and is even more efficient with scaling the number of algorithms in a workflow.