Hypertension (HT) is a modifiable risk factor for life-threatening cardiovascular diseases (CVDs) including coronary artery disease, heart failure or stroke. Despite significant progress in understanding of the pathophysiological mechanisms of the disease, the molecular pathways targeted by HT treatment still remain largely unchanged. This warrants the necessity for searching novel biomarkers, which are causally related to persistent high blood pressure (BP) and may be pharmacologically targeted. Data from large-scale biobanks, containing high-throughput genetic and biochemical data, such as OLINK and SomaScan-based proteomics or Nuclear Magnetic Resonance-based metabolomics, as well as novel analytical tools including Mendelian randomisation (MR) approach enabling genetic casual inference, may create new treatment opportunities for HT and related CVDs. MR analysis may constitute an additional proof for observational studies and facilitate selection of druggable targets for clinical testing and have been already used to nominate potentially causal biomarkers for HT and CVDs such as circulating glycine, branched-chain amino acids, insulin-like growth factor 1 or fibronectin 1. Using MR framework, genetic proxies for targets of already known drugs, such as statins, PCSK9 and ACE inhibitors, may additionally inform about potential side effects and eventually contribute to a more personalized medicine. Finally, genetic causal inference may disentangle independent, direct effects of correlated traits such as lipid classes or markers of inflammation on cardiovascular clinical outcomes such as atherosclerosis and HT. While several novel HT-targeting drugs are currently under clinical investigation (e.g. brain renin-angiotensin-aldosterone system inhibitors or endothelin-1 receptor antagonists), analysis of high-throughput proteomic and metabolomic data from well-powered studies may deliver novel druggable molecular targets for HT and associated CVDs.