Since decades de novo design of small molecules is intensively used and fragment-based drug discovery (FBDD) approaches still gain in popularity. Recent publications considering synthetically feasible de novo drug design underline the ongoing need for new methods. Continuous development of algorithms and tools are made, where a combination of intuitive usage, acceptable runtime, and a thoroughly evaluated workflow on large scale data sets is still a curiosity. Here, we present an intuitive approach for constrained synthetically feasible fragment growing. Starting from a fragment within its crystallized structure building blocks are attached via covalent bond formation to build up larger ligands. Iteratively, conformations are generated inside the binding site and scored to find the best suitable one. To cope with the combinatorial explosion of large flexible building blocks a novel dynamic adaptation algorithm is introduced. The technique achieves low runtimes while keeping high accuracies. The developed workflow is evaluated on a large-scale data set of 264 co-crystallized fragments with their corresponding elaborated ligands. Using our approach for fragment-based ligand growing, we were able to generate putative ligands within an RMSD of less than 2 Å compared to its crystallized structure. Additionally, we were able to show the benefit of a monolithic tethered docking like methodology compared to state of the art docking. We incorporated our method, NAOMInext, in a clearly arranged graphical user interface that assists the user by defining valuable constraints to improve and accelerate the sampling workflow. In combination with predefined synthetic reaction rules NAOMInext efficiently suggests ideas for the next generation of novel lead compounds.