Complex networks describe the interactions between a vast number of components and are the foundation of many natural, societal, and technological phenomena. At the molecular level, interactions take place in networks based on DNA, proteins, or metabolites. The discovery of recurrent patterns in biochemical networks (i.e., network motifs) , in particular, has sparked chemists to translate theoretical design principles that use feedback loops into practical molecular methods that enable the synthesis of out-of-equilibrium chemical reaction networks (CRNs). Despite significant progress, expanding the complexity in CRNs in a modular, theory-based way remains scarce. One of the main difficulties in the synthesis of CRNs resides in the requirement of the integral approach, involving organic synthesis and mathematical modelling. Advances in systems chemistry demonstrate that CRNs can be experimentally designed, rationalizing behaviour ubiquitous for living systems e.g., bistability, oscillations, and other transient behaviors. In all examples, mass-action models are used to support and validate their observed dynamics, demonstrating the need for mathematical modelling to guide the design of CRNs. Synthetic CRNs, however, cannot always be accurately modelled using mass-action kinetics due to complexity of the reaction mechanisms involved as well as limitations in the determination of rate constants thereof. The experimental framework for CRNs with more complex behaviour is gaining ground rapidly, and novel approaches that can capture their dynamics are needed.