diff --git a/examples/cpp/shared/CMakeLists.txt b/examples/cpp/shared/CMakeLists.txt
index 6e7cd123..5c6b3c1e 100644
--- a/examples/cpp/shared/CMakeLists.txt
+++ b/examples/cpp/shared/CMakeLists.txt
@@ -18,19 +18,28 @@ project(svs_shared_library_example
     LANGUAGES CXX
 )
 
-# Other AVX versions can be found at https://github.com/intel/ScalableVectorSearch/releases.
-set(SVS_URL "https://github.com/intel/ScalableVectorSearch/releases/download/v0.0.9/svs-shared-library-0.0.9.tar.gz")
-
-include(FetchContent)
-FetchContent_Declare(
-  svs
-  URL "${SVS_URL}"
-)
-FetchContent_MakeAvailable(svs)
-
-list(APPEND CMAKE_PREFIX_PATH "${svs_SOURCE_DIR}")
-find_package(svs REQUIRED)
-find_library(SVS_SHARED svs_shared_library)
+# Try to find SVS from system/conda/pip installation first
+find_package(svs QUIET)
+
+if(NOT svs_FOUND)
+    # If sourcing from pip/conda, the following steps are not necessary, simplifying workflow
+    # If not found, download tarball from GitHub release and follow steps to fetch and find
+    set(SVS_URL "https://github.com/intel/ScalableVectorSearch/releases/download/v0.0.9/svs-shared-library-0.0.9.tar.gz")
+    
+    message(STATUS "SVS not found in system, downloading from: ${SVS_URL}")
+    
+    include(FetchContent)
+    FetchContent_Declare(
+        svs
+        URL "${SVS_URL}"
+    )
+    FetchContent_MakeAvailable(svs)
+    
+    list(APPEND CMAKE_PREFIX_PATH "${svs_SOURCE_DIR}")
+    find_package(svs REQUIRED)
+else()
+    message(STATUS "Found SVS: ${svs_DIR}")
+endif()
 
 set(SVS_CXX_STANDARD 20)
 SET(CMAKE_CXX_FLAGS  "-O3 -DNDEBUG -std=gnu++20 -march=native -mtune=native -Werror -Wall -Wextra -Wpedantic" )
@@ -41,7 +50,10 @@ set(DATA_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/../../../data/test_dataset")
 function(create_example_executable exe file)
     add_executable(${exe} ${file})
     target_compile_definitions(${exe} PRIVATE SVS_DATA_DIR="${DATA_DIRECTORY}")
-    target_link_libraries(${exe} PUBLIC ${SVS_SHARED} svs::svs)
+    target_link_libraries(${exe} PUBLIC 
+        svs::svs_shared_library
+        svs::svs
+    )
 endfunction()
 
 create_example_executable(shared shared.cpp)
diff --git a/examples/cpp/shared/README.md b/examples/cpp/shared/README.md
index 4dc96605..5adcc122 100644
--- a/examples/cpp/shared/README.md
+++ b/examples/cpp/shared/README.md
@@ -14,17 +14,54 @@
   ~ limitations under the License.
 -->
 
-These examples utilize LVQ and LeanVec interfaces which are available when linking to a SVS shared/static library, which are published with [releases](https://github.com/intel/ScalableVectorSearch/releases). Note that these examples will _not_ run after building the open source codebase without the shared/static library. These examples include:
+These examples utilize LVQ and LeanVec interfaces which are available when linking to a SVS shared/static library, which are published with [releases](https://github.com/intel/ScalableVectorSearch/releases), as a tarball, pip wheel, or conda package. Note that these examples will _not_ run after building the open source codebase without the shared/static library. These examples include:
 - [example_vamana_with_compression.cpp](./example_vamana_with_compression.cpp): Demonstrates building, searching, saving, and reloading an index with a LeanVec-compressed dataset.
 - [example_vamana_with_compression_lvq.cpp](./example_vamana_with_compression_lvq.cpp): Demonstrates building, searching, saving, and reloading an index with a LVQ-compressed dataset.
 - [example_vamana_with_compression_dynamic.cpp](./example_vamana_with_compression_dynamic.cpp): Demonstrates building, searching, saving, and reloading a dynamic index (allows vector insertions and deletions over time) with a LeanVec-compressed dataset.
 
-See [CMakeLists.txt](./CMakeLists.txt) for details on linking to the SVS shared library and follow the commands below to compile and use the SVS shared library to run shared.cpp example:
+See [CMakeLists.txt](./CMakeLists.txt) for details on linking to the SVS shared library.
 
+## Running the examples
+
+The CMakeLists.txt is set up to detail usage of all options (tarball, pip, conda), and will prioritize utilization in the following order:
+
+1. **System/Conda/Pip installation** - If SVS is installed in a standard location that CMake can find
+2. **GitHub Release download** - If not found, it will  download the tarball from GitHub
+
+### Option 1: Using libsvs in a conda environment
+
+Install the `libsvs` package:
+```bash
+conda install -c https://software.repos.intel.com/python/conda libsvs
+
+mkdir build
+cd build
+CC=gcc-11 CXX=g++-11 cmake ../
+make -j
+./example_vamana_with_compression_dynamic
 ```
+
+### Option 2: Using pip-installed libsvs
+
+Install the `libsvs` package (currently only available from GitHub releases) and ensure CMake can find it by setting `CMAKE_PREFIX_PATH`:
+```bash
+pip install https://github.com/intel/ScalableVectorSearch/releases/download/v1.0.0-dev/libsvs-0.0.10+NIGHTLY20251023.184-py3-none-any.whl
+
+mkdir build
+cd build
+# Note that pip packages require setting CMAKE_PREFIX_PATH to find the library, conda handles this automatically
+CC=gcc-11 CXX=g++-11 cmake -DCMAKE_PREFIX_PATH=$(python -c "import libsvs; print(libsvs.get_cmake_prefix_path())") ..
+make -j
+./example_vamana_with_compression_dynamic
+```
+
+### Option 3: Using shared library tarball
+
+If `libsvs` is not installed, CMake will download the tarball (see [CMakeLists.txt](./CMakeLists.txt) for the necessary steps here):
+```bash
 mkdir build
 cd build
 CC=gcc-11 CXX=g++-11 cmake ../
 make -j
-./shared
+./example_vamana_with_compression_dynamic
 ```